Subjects: Psychology >> Developmental Psychology submitted time 2023-03-28 Cooperative journals: 《心理科学进展》
Abstract： Construal Level Theory considered that self-control was broadly conceptualized as making decisions and acting in accordance with global, high-level construal of the situation rather than local, low-level construal. Researches concerning temporal discount, persistence, healthy behaviors and prospective self-control showed that high construal level promotes self-control. Moreover, the effect of high-level construal on self-control was mediated by attitudes toward temptations and asymmetric temptation-goal. The effect is modulated by the characteristics of task and goal (e.g. goal value and difficulty, valence, response style, manifestation of goals) and presence of high level construal cue (explicit or implicit). In summary, the present approach, which considers self-control as a construal-dependent decision, extended self-control Dual-Model and Ego-Depletion Model and provided some more possible psychological mechanisms. Therefore, further research should focus on mechanisms of the positive construal level and self-control association, investigate effects of psychological distance on such association and look into this association from longitudinal perspective.
Subjects: Psychology >> Social Psychology submitted time 2023-03-28 Cooperative journals: 《心理科学进展》
Abstract： How semantics are represented in human brains is a central issue in cognitive neuroscience. Previous studies typically detect semantic information by manipulating the properties of stimuli or task demands, or by asking a group of participants to judge the stimuli according to several given dimensions or features. Despite having brought valuable insights into the neurobiology of language, these approaches have some limitations. First, the experimental approach may only provide a coarse depiction of semantic properties, while human judgment is time-consuming and the results may vary substantially across subjects. Second, the conventional approach has difficulty quantifying the effect of context on word meaning. Third, the conventional approach is unable to extract the topic information of discourses, the semantic relations between the different parts of a discourse, or the semantic distance between discourses. The recently-developed natural language processing (NLP) techniques provide a useful tool that may overcome the above-mentioned limitations. Grounded on the distributional hypothesis of semantics, NLP models represent meanings of words, sentences, or documents in the form of computable vectors, which can be derived from word-word or word-document co-occurrence relationships, and neural networks trained on language tasks. Recent studies have applied NLP techniques to model the semantics of stimuli and mapped the semantic vectors onto brain activities through representational similarity analyses or linear regression. Those studies have mainly examined how the brain (i) represents word semantics; (ii) integrates context information and represents sentence-level meanings; and (iii) represents the topic information and the semantic structure of discourses. Besides, a few studies have applied NLP to untangle sentences’ syntactic and semantic information and looked for their respective neural representations. A consistent finding across those studies is that, the representation of semantic information of words, sentences and discourses, as well as the syntactic information, seems to recruit a widely distributed network covering the frontal, temporal, parietal and occipital cortices. This observation is in contrast to the results from conventional imaging studies and lesions studies which typically report localized neural correlates for language processing. One possible explanation for this discrepancy is that NLP language models trained on large-scale text corpus may have captured multiple aspects of semantic information, while the conventional experimental approach may selectively activate a (or several) specific aspects of semantics and therefore only a small part of the brain can be detected. Though NLP techniques provide a powerful tool to quantify semantic information, they still face some limitations when being applied to investigate semantic representations in the brain. Firstly, embeddings from NLP models (especially those from a deep neural network) are uninterpretable. Secondly, models differ from each other in training material, network architecture, amount of parameters, training tasks and so on, which may lead to potential discrepancies among research results. Finally, model training procedures differ from how humans learn language and semantics, and the inner computational and processing mechanism may also be fundamentally different between NLP models and the human brain. Therefore, researchers need to select a proper model based on research questions, test the validity of models with experimental designs, and interpret results carefully. In the future, it is promising to (i) adopt more informational semantic representation methods such as knowledge-graph and multimodal models; (ii) apply NLP models to assess the language ability of patients; (iii) improve the interpretability and performance of models taking advantages of cognitive neuroscience findings about how human process language.
Subjects: Psychology >> Social Psychology submitted time 2023-03-28 Cooperative journals: 《心理科学进展》
Abstract： Aberrant responses have been repeatedly reported in psychological and educational measurement. If traditional measurement models or methods (e.g., item response theory, IRT) are applied to data sets contaminated by aberrant responses, parameter estimates may be biased. Therefore, it is necessary to identify aberrant responses and to reduce their detrimental effects. In the literature, there are two traditional response time (RT)-based methods to detect aberrant responses: RT threshold method and RT residual method. The focus of these methods is to find a threshold of RT or RT residual. If a RT or RT residual is remarkably less than the threshold, this response should be regarded as an aberrant response with extremely short RT (e.g., speededness, rapid-guessing), and consequently does not provide information about the test taker’s latent trait. Afterwards, down-weighting strategy, which tries to limit the influence of aberrant responses on parameter estimation by reducing their weight in the sample, can be applied. The mixture model method (MMM), is a new method proposed to handle data contaminated by aberrant responses. This method applies the accommodating strategy, which is to extend a model in order to account for the contaminations directly. MMM shows more advantages in terms of: (1) detecting aberrant responses and obtaining parameter estimates simultaneously, instead of two steps (detecting and down-weighting); (2) precisely recovering the severity of aberrant responding. There are two categories of MMM. The first category of methods assumes that the classification (i.e., whether the item is answered normally or aberrantly) can be predicted by RT. While the second category is a natural extension of van der Linden’s (2007) hierarchical model, which models responses and RTs jointly. In this method, the observed RT, as well as the correct response probability of each item-by-person encounter can be decomposed to RT (or probability) caused by normal response and that caused by aberrant response according to the most important difference between the two distinct behaviors. This method leads to more precisely estimated item and person parameters, as well as excellent classification of aberrant/normal behavior. First, this article compares the basic logic of the two traditional RT-based methods and MMM. Aberrant responses are regarded as outliers in both RT threshold method and RT residual method. Therefore, they rely heavily on the severity of aberrance. If data set is contaminated by aberrant responses seriously, the observed RT (or RT residual) distribution will be different from the expected distribution, which in turn leads to low power and sometimes high false detection rate. On the other hand, MMM, which assumes that both observed RT and correct response probability follow a mixture distribution, treats aberrant and normal responses equally. In that way, it has little reliance on the severity of aberrance. In addition to that, MMM can apply to the situation when all the respondents actually respond regularly in theoretic. In that situation, all the responses are assumed to be classified into one category. Second, this article summarizes the disadvantages of the three methods. MMM has three primary limitations: (1) it usually relies heavily on strong assumptions, which means that it may not perform well if these assumptions are violated; (2) low proportion of aberrant response may lead to convergence problem and model identification problem; (3) it is quite complex and time-consuming. In all, practitioners should choose a proper method according to the characteristics of tests and categories of aberrant responses (e.g., rapid-guessing, item with preknowledge, cheating). In the end, this article suggests future researches may investigate the performance of MMM when its assumptions are violated or data consists of more types of aberrant response patterns. Fixing item parameter estimates, proposing some index to help choosing suitable methods, are encouraged to improve the efficiency of MMM.
Subjects: Psychology >> Social Psychology submitted time 2023-03-28 Cooperative journals: 《心理科学进展》
Abstract： Computer-based problem-solving tests can record respondents’ response processes when they explore tasks and solve problems as process data, which is richer in information than traditional outcome data and can be used to estimate latent abilities more accurately. The analysis of process data in problem solving tests consists of two main steps: feature extraction and process information modeling. There are two main approaches to extracting information from process data: top-down and bottom-up method. The top-down method refers to developing rubrics by experts to extract meaningful behavioral indicators from process data. This approach extracts behavioral indicators that are closely related to the conceptual framework, have interpretable and clear scores, and can be analyzed directly using psychometric models, as is the case with items in traditional tests. However, such indicator construction methods are laborious and may miss unknown and previously unnoticed student thought processes, resulting in a loss of information. In contrast, the bottom-up method refers to the use of data-driven approaches to extract information directly from response sequences, which can be divided into the following three categories according to their processing ideas: (1) methods that analogize response sequences to character strings and construct indicators by natural language processing techniques; (2) methods that use dimensionality reduction algorithms to construct low-dimensional numerical feature vectors of response sequences; and (3) methods that use directed graphs to characterize response sequences and use network indicators to describe response features. Such methods partially address the task specificity in establishing scoring rules by experts, and the extracted features can be used to explore the behavioral patterns characteristic of different groups, as well as to predict respondents’ future performance. However, such methods may also lose information, and the relationship between the obtained features and the measured psychological traits is unclear. After behavioral indicators have been extracted from process data, probabilistic models that model the relationship between the indicators and the latent abilities can be constructed to enable the estimation of abilities. Depending on whether the model makes use of sequential relationships between indicators and whether continuously interpretable estimates of latent abilities can be obtained, current modeling methods can be divided into the following three categories: traditional psychometric models and their extensions, stochastic process models, and measurement models that incorporate the idea of stochastic processes. Psychometric models focus on estimates of latent abilities but are limited by their assumption of local independence and cannot include sequential information between indicators in the analysis. The stochastic process model focuses on modeling the response process, retaining information about response paths, but with weaker assumptions between indicators and underlying structure, and is unable to obtain continuous and stable estimates of ability. Finally, psychometric models that incorporate the idea of stochastic processes combine the advantages of both taking the sequence of actions as the object of analysis and having experts specify indicator coefficients or scoring methods that are consistent with the direction of abilities, thus allowing continuous interpretable estimates of abilities to be obtained while using more complete process information. However, such modeling methods are mostly suitable for simple tasks with a limited set of actions thus far. There are several aspects where research on feature extraction and capability evaluation modeling of process data could be improved: (1) improving the interpretability of analysis results; (2) incorporating more information in feature extraction; (3) enabling capability evaluation modeling in more complex problem scenarios; (4) focusing on the practicality of the methods; and (5) integrating and drawing on analytical methods from different fields.
Subjects: Psychology >> Social Psychology submitted time 2023-03-28 Cooperative journals: 《心理科学进展》
Abstract： Learning and memory are the foundation of individual survival and development. Improving learning and memory is the focus of psychology and neuroscience. Recently, many studies have revealed that rewards facilitate declarative memory, and the influence of reward on declarative memory has become a hot research topic. Rewards are related to the midbrain dopamine system, including areas such as the ventral tegmental area, the substantia nigra, and the ventral striatum, with dopamine as the relevant vital neurotransmitter. The hippocampus and adjacent cortices play an essential role in the encoding, consolidation, and retrieval of declarative memory. The midbrain reward system and the memory system (i.e., the ventral tegmental area and hippocampus) are connected both structurally and functionally. Rewards can act on memory encoding and consolidation, thus promoting memory performance. During the encoding and consolidation stages, rewards promote memory via the interaction of various brain systems (i.e., the reward system, the attention control system, and the memory system). The impact of rewards during these two stages involves different cognitive processes and neural mechanisms. During the memory encoding stage, rewards affect both intentional and incidental memories. According to the intentional memory paradigm, participants are explicitly informed that a reward is contingent upon memory performance in a subsequent test when they encode the items. In this paradigm, this performance-dependent reward triggers the reward system and involves the attentional control system, and these two systems modulate the memory system, allocating more cognitive resources to reward-related items, thereby promoting memory with respect to these items. According to the incidental memory paradigm, rewards accompany some items during the encoding phase but are independent of memory performance regarding these items in the subsequent test. In this paradigm, participants are not aware of the subsequent memory test before they process the information; thus, the reward enhancement effect on memory can mainly be explained in terms of the interaction between the memory system and the reward system. However, even though participants do not intentionally allocate cognitive resources in this context, the rewarded items themselves automatically attract attention. Therefore, the influence of attention and the involvement of the attentional control system cannot be excluded entirely. During the memory consolidation stage, the addition of a reward also affects memory performance, and the influence of attention can be excluded entirely at this stage; thus, the enhancement effect on memory consolidation can be explained in terms of pure reward. During the consolidation stage, the hippocampal memory system reactivates the encoded content. The reward facilitates dopamine release, modulates the hippocampal processing of reward-related items, and enhances the reactivation of reward-related items, thus directly affecting memory performance without the involvement of the attentional control system. Future research should focus on the following three areas. First, rewards affect behavior not in terms of a simple and pure enhancement pattern but rather according to a complex pattern. The factors and mechanisms that impact the effect of rewards on memory must be clarified, and a more consummate model of the reward effect on memory should be developed to provide more accurate guidance for learning in real life (i.e., a model of when and how rewards should be applied in education). Second, only a few studies have investigated the effects of rewards during the memory consolidation and retrieval stages. More attention should be given to the effects of rewards during these two stages (i.e., the ways in which rewards affect consolidation during different states as well as memory retrieval and subsequent memory). Finally, most studies have investigated the effects of external rewards on memory, and future research should focus on the impacts of internal rewards on learning and memory. We should compare the behavioral patterns and neural mechanisms associated with the effects of internal and external rewards on memory and test the interaction effect of internal and external rewards on memory.
Subjects: Psychology >> Social Psychology submitted time 2023-03-28 Cooperative journals: 《心理科学进展》
Abstract： With the booming development of digital economy and digital technology, digital transformation has changed from an "optional" choice for some leading enterprises to a "mandatory" requirement for more enterprises. However, many companies face several problems in the digital transformation process such as slow performance growth and insufficient transformation sustainability. Among possible reasons for these problems, the misfit between employees' digital competencies, digital technologies, and digital job demands (i.e., individual-task-technology misfit) is the main one. Therefore, understanding how employees can proactively change the digital job environment and increase individual-task-technology fit to improve job performance has both theoretical and practical implications. Based on the job crafting research and individual-task-technology fit theory, this project proposes a new concept called digital job crafting and explore the mechanisms for the effect of digital job crafting on employees' job performance. Meanwhile, from the perspectives of colleagues, leaders, and organizational structure, this project will examine digital job crafting support, digital leadership, and organizational formalization as potential boundary conditions of the relationship between digital job crafting and job performance. We intend to test our hypotheses using focus group interviews, case studies, multi-source and multi-wave surveys, and daily diary surveys. This project will contribute to expanding digital job research from a proactive adaptation perspective and initiate new research themes for job crafting research. It also provides theoretical guidance and practical intervention plans for employees to proactively adapt to digital transformation and gain digital intelligence dividends.
Subjects: Psychology >> Social Psychology submitted time 2023-03-28 Cooperative journals: 《心理科学进展》
Abstract： Symptoms of Autistic Spectrum Disorders (ASD) can manifest as early as infancy, and the earlier detection and intervention can lead to better therapeutic results. The traditional screening and diagnosis of autism comes from professionals, which is highly subjective and time-consuming, leading to misdiagnosis or missing the optimal intervention time. In recent years, with the rapid development of artificial intelligence and the accumulation of clinical data on autism, intelligent recognition methods for autism and its early symptoms have developed rapidly. This paper summarizes the research on intelligent recognition of autistic infants in the past decade, and divides the research into six sub-areas based on the data types used in the research: 1) recognition based on classical task behavior data; 2) recognition based on facial expression and emotional data; 3) recognition based on eye gaze data; 4) recognition based on brain image data; 5) recognition based on motor control and movement pattern data; 6) recognition based on multimodal data. The current technology is to use non-contact vision system and sensory devices to collect infants’ behavioral data, such as facial expression, head and limb movement, eye movement, brain image. Researchers usually develop risk behavior detection algorithms and build machine learning or deep learning models for automatic recognition according to task objectives and data characteristics. At present, it can reach the precision of scale tools and manual evaluation. The current modeling trend is to use a multimodal fusion framework to build prediction models based on the complementary relationship between multimodal information, feature transformation and representation patterns of autistic infants, which is expected to further improve the recognition accuracy. In the future, researchers should focus on building an intelligent medical screening and diagnosis system for early autism, developing screening tools for infants and young children, establishing a refined diagnosis method for autism combined with brain imaging technology, and building an intelligent recognition model for autistic infants by integrating multimodal data. In addition, to carry out high-quality intelligent recognition research, a large-scale database of autism and high-risk infants and corresponding behavioral characteristic database should be established as soon as possible, and risk behaviors were marked in coarse and fine granularity according to the early behavioral diagnostic criteria of autism. Currently, in the face of the contradiction between the large demand for model training data and the lack of autistic samples, researchers can first try to use small sample learning methods, such as model fine-tuning, data augmentation, transfer learning.
Subjects: Psychology >> Social Psychology submitted time 2023-03-28 Cooperative journals: 《心理科学进展》
Abstract： The awe is though as a complex awe experience, even a self-transcendent emotion, caused by something mysterious, vast and beyond the current cognitive schema. The psychological research of awe has recently received great attention, but the nature of awe or the psychological mechanism is still unclear. To address the scientific question of “what is the psychological process of awe”, the existing awe-related literature is reviewed and a Psychological Model of Awe is proposed to explain the psychological process of awe and its cognitive neural mechanism on the basis of clarifying the evolution of the concept of aw, related theoretical development and empirical research of awe. It has been discovered that the generation of awe involves the psychological processes including expectation estimate, outcome evaluation and self-transcendent. Vastness and the need for accommodation are the two core factors of awe which also has two dimensions including internal and external vastness, and positive and negative affection. Future research should focus on the development of awe-related assessment, verification of awe theory and comparative analysis of awe behavior in different cultural people by carrying out multi-disciplinary basic and clinical research.
Subjects: Psychology >> Social Psychology submitted time 2023-03-28 Cooperative journals: 《心理科学进展》
Abstract： With the development of data-collection technics and increasing complexity of study designs, nested data widely exists in psychological research. Linear mixed-effects models, unfortunately with an unreasonable hypothesis that the residual variances are homogenous, are generally used in nested data analysis. Meanwhile, Mixed-Effects Location-Scale Models (MELSM) has become more and more popular, because they can handle heterogenous residual variances and are able to add predictors for the two substructures (i.e., mean structure denoted as location model and variance structure denoted as scale model) in different levels. MELSM can avoid estimation bias due to inappropriate assumptions of homogenous variance and explore the relationship among traits and simultaneously investigate the inter- and intra-individual variability, as well as their explanatory variables. This study, aims at developing the methods of model construction and sample size planning for MELSM, using simulated studies and empirical studies. In detail, the main contents of this project are as follows. Study 1 focuses on comparing and selecting candidate models based on Bayesian fit indices to construct MELSM, taking into consideration the estimated method for complicated models. We propose that model selection for location model and scale model can be completed sequentially. Study 2 explores the method of sample size planning for MELSM, according to both power analysis (based on Monte Carlo simulation) and the accuracy in parameter estimation analysis (based on the credible interval of the posterior distribution). Adequate sample size is required for both the power and the accuracy in parameter estimation. Study 3 extends the sample size planning method for MELSM to better frame the considerations of uncertainty. By specifying the prior distribution of effect sizes, repeating sampling and selecting model based on the robust Bayesian fit index suggested by Study 1, three main sources of uncertainty can be well controlled: the uncertainty due to unknown population effect size, sampling variability and model approximation. With the simulated study results, we are able to provide reliable Bayesian fit indices for MELSM construction, and summary the process of sample size planning for MELSM in both determinate and uncertain situations. Moreover, Study 4 illustrates the application of MELSM in two empirical psychological studies and verifies the operability of the conclusions of the simulated studies in practice. The unique contribution of this paper is to further promote the methods of model construction and sample size planning for MELSM, as well as provide methodological foundation for researchers. In addition, we plan to integrate the functions above to develop a user-friendly R package for MELSM and provide a basis for promotion and application of MELSM, which help researchers make sample size planning, model construction and parameter estimation for MELSM easily, according to their specification. If these statistical models are widely implemented, the reproducibility and replicability of psychological studies will be enhanced finally.
Subjects: Psychology >> Social Psychology submitted time 2023-03-28 Cooperative journals: 《心理科学进展》
Abstract： Developmental dyslexia (hereafter referred as "dyslexia") will not only affect the lifelong development of individuals but also impose an additional financial burden on society. Digging into the relevant neural mechanisms contributes to the early prediction and intervention of dyslexia. Established models of the neural bases of dyslexia primarily focused on the cerebrum. In recent years, extensive studies have shown that dyslexia is also associated with cerebellar abnormalities. However, it remains unclear about the relationships between the two. By summarizing recent findings, we found that the cerebellum could play multiple roles in reading. First, it could influence reading in different ways. Cerebellar dysfunctions could impair reading by affecting motor or motor-related skills (such as oculomotor control, automatization, or articulation), or by disturbing linguistic-related processes (such as phonological or semantic processing). Second, different subtypes of dyslexia are associated with abnormalities in distinct cerebellar regions. For example, dyslexic readers with automatization deficits showed abnormal neural activities in the anterior parts of the cerebellum, which were responsible for motor processing, whereas dyslexic readers suffering from visual and phonological deficits were associated with the abnormalities in the gray matter volume of the posterolateral areas of the cerebellum, which were mainly responsible for high-level cognitive processing. These results indicate that the relationship between dyslexia and the cerebellum is not unitary. There may exist multiple cerebellar areas being targeted by dyslexia, which also contribute differently to reading. The causal relationships between cerebellar abnormalities and dyslexia might be bi-directional. Previous literature found that structural deficits in the posterolateral parts of the cerebellum were only associated with dyslexia compared to other development disorders (i.e., ADHD, autism) that may coexist with dyslexia. This result suggests that neural abnormalities in these areas were due to deficits in reading abilities rather than other comorbidities. Additionally, these regions vary in their causal relationships with dyslexia. For example, activation in the anterior parts of the right lobule VI, responsible for motor processing, showed greater activation or functional connectivity with the cerebrum in dyslexic readers compared to normal readers. These increased neural activities may be the compensatory mechanisms of dyslexia and a by-product of reading difficulties. In contrast, neural activities of the cerebellar areas responsible for linguistic processing (i.e., the right lobule VII) could predict future reading abilities, indicating that the functional state of the cerebellum in early developmental stages may influence reading development. Moreover, functional deactivations in the cerebellar linguistic areas have been observed in preschool readers with a high risk of dyslexia, suggesting that cerebellar abnormalities have occurred before formal reading instruction. These results jointly support that cerebellar abnormalities may be the cause of dyslexia. The results mentioned above illustrate that the cerebellum is more than a reading-related hub. There could be multiple cerebellar regions that are engaged in reading, with different regions supporting different cognitive processes and having distinct causal relationships with dyslexia. Accordingly, we introduced the "cerebro-cerebellar mapping hypothesis of word reading", which proposed that reading-related regions in the cerebellum map to their functional correspondence areas in the cerebrum. Regions with the same functions across the cerebrum and cerebellum synchronized in neural activities and collaborated during reading. Dysfunctions of this collaboration may lead to dyslexia. This new framework aims to reveal the relationship between reading, the cerebellum, and the cerebrum from a new perspective, and offers important insights into the neural mechanism of dyslexia and the role of the cerebellum in high-level cognitive processing.
Subjects: Psychology >> Social Psychology submitted time 2023-03-28 Cooperative journals: 《心理科学进展》
Abstract： Most languages of the world have a high proportion of morphologically complex words that are composed of two or more morphemes. When processing morphologically complex words, readers can choose to access the whole word meaning directly from the mental lexicon or to use morphological rules to construct the word semantics. Therefore, the exploration of the morphologically complex word processing mode could help us to promote the understanding of the language processing mechanism. In the past 30 years, the masked priming paradigm and lexical decision task have been widely used to study the mechanism of morphological complex words both in the first language (L1) and second language (L2) research. There is a consensus that native speakers could access the whole word meaning by combing morphemes, however, despite an increasing amount of second language research regarding this issue, the morphologically complex word processing mechanism for the late bilinguals who acquire the second language after the critical period (5 years old) is still in debate and shows a different pattern from native speakers. This mainly lies in two aspects: First, the morphological priming effect in the second language studies is very unstable, indicating that the ability of late second language learners to use morphological rules may be affected by many factors and the application of morphological rules is not as efficient as native speakers. Second, compared with native speakers, the late second language learners are more inclined to rely on the orthographic information than the morphological structure information in early word recognition, which is reflected by when the prime and target share the similar in orthography, there is no semantic competition between them to offset the promotion effect brought by the similarity of the orthography. Based on the Complementary Learning Systems account and Episodic L2 Hypothesis, the possible processing mechanism of L2 morphologically complex words is proposed and the divergence of previous L2 research is explained under this theoretical framework. We propose that different from native speakers, the late bilinguals mainly acquire the second language knowledge in an explicit way (i.e., classroom introduction), which may lead to the abstract morphological information in the neocortex is not formed. Therefore, when late second language learners process complex morphological words, the abstract morphological rules through the neocortical mechanism could not be used to promote the morphologically complex words’ processing, but a temporary morphological rule through the hippocampal mechanism could be involved in this process. Since the extraction of morphological rules through the hippocampus mechanism needs to be reconstructed and then real-time reconstruction of morphological rules takes a certain amount of time, which leads to the instability of the morphological priming effect and the lower efficiency of the application of the morphological rules. In addition, according to the Episodic L2 Hypothesis, for late second language learners, words are stored in the episodic memory system where these representations are irrelevant. Therefore, there is no competition at the meaning level between the prime and target when they are similar in orthography, which also explains why there is a significant from priming effect for the late L2 learners at least in lexical decision task. The evidence from the cognitive neuroscience area is needed to test the rationality of our hypothesis in the future. Secondly, eye movement techniques could be adopted in future research to investigate the processing mechanism of morphologically complex words for second language learners under more natural reading conditions. Finally, future research also needs to explore the influences of morphological family size on the processing of L2 morphologically complex words, as well as reveal the neural mechanism underlying L2 morphologically complex words.
Subjects: Psychology >> Social Psychology submitted time 2023-03-27 Cooperative journals: 《心理学报》
Abstract： The present study aimed to explore a new method of measuring shyness based on 1306 elementary school students’ online writing texts. A supervised learning method was used to map students' labels (tagged by their results of scale) with their text features (extracted from online writing texts based on a psychological dictionary) to build a machine learning model. Key feature sets for different dimensions of shyness were built and a machine learning model was constructed based on the selected feature to achieve automatic prediction. The labels were obtained through “National School Children Shyness Scale” completed online by elementary students. The scale includes three dimensions of shyness: shy behavior, shy cognition and shy emotion. Students with Z-scores of each dimension over 1 were labeled as shy and others were labeled as normal. Students’ online writing texts were collected from "TeachGrid" (https://www.jiaokee.com/), an online learning platform wherein students writing texts. The dictionary applied in the present study was Textmind, a widely used Chinese psychological dictionary developed based on Linguistic Inquiry and Word Count (LIWC). The dictionary was compiled mainly based on the corpus of adults. To ensure the validity of extracted features, we modified the original dictionary by expanding the categories and vocabulary with the real writing text of elementary students. The revised dictionary contained 118 categories. Features were extracted based on the revised dictionary. Chi-square algorithm was applied to identify the features that can distinguish between shy and normal groups to the greatest extent. Three sets of key features confirmed a significant lexical difference between shy and normal individuals. Among the selected features, some were shared by multiple dimensions reflecting the universal textual expression of shy individuals (e.g., The average number of words per sentence and the frequency of social words of shy individuals were less than that of normal counterparts.), and there were certain features reflected the unique characteristics of certain dimension (Perception words predicted shy behavior reflecting that high shy behavior individuals frequently felt being watched). Based on the selected features, Python 3.6.2 was used to construct the six prediction modes: Decision Tree, Random Forest, Support Vector Machine, Logistic Stitch Regression, K-Nearest Neighbor and Multilayer Perceptron. Overall, random forests have achieved the best results in the present study. The F1 score was 0.582, 0.552 and 0.545 for behavior cognition and emotion showing the feasibility of automatically predicting shyness characteristics of elementary school students based on textual language. The implication of word embedding, and deep learning models would improve the final prediction.#shyness, online writing, psychological dictionary, text mining, language style model
Subjects: Psychology >> Social Psychology submitted time 2023-03-27 Cooperative journals: 《心理学报》
Abstract： It is well-known that attentional orienting is contingent on the features of attentional settings, and in recent years, an increasing number of studies have supported that the meaningful contingency between cue and target stimuli modulates spatial attention. However, the spatial distribution of meaning-guided attentional orienting has not been thoroughly elucidated, especially in noncentral space. To address this issue, we examined the attentional orienting effects by establishing the meaningful contingency between the objects and how the attentional orienting was affected by the nature of the objects. Furthermore, the attentional distribution in the noncentral fields was analyzed. A modified spatial cueing paradigm was employed in the current study. In Experiment 1, cues were presented as strawberry or watermelon sketches, and targets were presented in red or green. The participants were asked to discriminate the location of the gap of the target square in different cue-target blocks. Experiment 2 was identical to Experiment 1, except that the cues were white Chinese characters, “红” (meaning red) or “绿” (meaning green), and the number of possible positions was increased from four to six. Experiment 3 was identical to Experiment 2, except that the cue and target stimuli were swapped, where cues were presented in red or green and targets were Chinese characters in white. The results indicated that the inhibition effects were found in the lower spatial field and the increasing capture effects were found in the left and right and the upper spatial fields when the sketches were adopted as cues in Experiment 1. In Experiment 2, it was found that there was a general trend of inhibition and capture effects from the lower to upper locations, but only part of the inhibition effects reached significance when the number of the positions was increased and the Chinese character cues were employed. Experiment 3 replicated the results obtained in Experiment 1 and Experiment 2 when color cues were utilized, but more robust inhibition and capture effects were obtained. The results of this study indicated that (1) the meaningful contingency between the objects guided the visuospatial attentional orienting, highlighting the inhibition and capture effects in different visuospatial fields; (2) the nature of the object modulated the meaningful-contingent attentional orienting, showing that the more vivid the object was, the more modulated it was, whereas the more abstract the object was, the less modulated it was; and (3) the meaningful-contingent attentional orienting was performed regularly in different visual fields, highlighting the location-based inhibition and capture from the lower to the upper fields.
Subjects: Psychology >> Social Psychology submitted time 2023-03-27 Cooperative journals: 《心理学报》
Abstract： Mediation and moderation analyses are commonly used methods for studying the relationship between an independent variable (X) and a dependent variable (Y) in conducting empirical research. To better understand the relationships among variables, there is an increasing demand for a more general theoretical framework that combines moderation and mediation analyses. Recently, statistical analysis of mediated moderation (meMO) effects has become a powerful tool for scientists to investigate complex processes. However, the traditional meMO model is formulated based on the homoscedasticity assumption, which is most likely to be violated when moderation effects exist. In addition, routinely reporting effect sizes has been recommended as the primary solution to the issue of overemphasis on significance testing. Appropriate effect sizes (ES) for measuring meMO effects are very important in reporting and interpreting inferential results. However, there does not exist an effective measure that allows us to answer the question regarding the extent to which a variable Z moderates the effect of X on Y via the mediator variable (M) in the meMO model. The article is organized as follows. First, the two-level moderated regression model proposed by Yuan, Cheng, & Maxwell (2014) was extended to a two-level mediated moderation (2meMO) model with single-level data, the statistical path diagram was structured according to the conceptual model and the equations. Second, several effect sizes were developed for the 2meMO effect by decomposing the total variance of the moderation effect. Third, to estimate the parameters of the 2meMO model and the ES measures of the meMO effects, we developed a Bayesian estimation method to estimate the parameters of the 2meMO model. Fourth, a Monte Carlo simulation study was conducted to evaluate the performance of the 2meMO model and the proposed ES measures against those with the meMO model. Finally, we illustrate the application of the new model and measures with a real data example. The simulation results indicate that the size of bias and MSE for parameter estimates are small under both meMO and 2meMO models whether the homoscedasticity assumption hold or not. The results of the coverage rate of the 95% CI for difmoMEdifmoMEdi{{f}_{moME}} following 2meMO is comparable to those following meMO when the variance of moderation error is zero, which is the assumption the meMO model is based. However, when the moderation-error variance is nonzero, 2meMO yields more accurate estimates for difmeMOdifmeMOdi{{f}_{meMO}}. than meMO does, the advantages of 2meMO over meMO become more obvious as the moderation-error variance increases. The results of Type I error rate indicate that 2meMO controls Type I error rather well, and the rates are close to 0.05 or below 0.05 under all the conditions. However, the Type I error rates of meMO tend to be higher than 0.05 when the moderation-error variance is nonzero. The power rates following the meMO and 2meMO models are comparable for the medium or large sample size, or when there is a large difference in meMO effects. While the value of power following 2meMO is slightly lower than that following meMO at small sample se, this result is mostly due to the inflated Type I error rate of meMO, and larger sample sizes and the smaller moderation-error variances correspond to more accurate estimates of ϕ(f)meMOϕ(f)meMO\phi _{meMO}^{(f)}. The results also indicate that, when the homoscedasticity assumption of the meMO model is satisfied, the effect size estimates following the two models are about the same. However, when the moderation-error variance is not zero, the results following 2meMO are more accurate than those following meMO. In summary, the article developed a 2meMO model with single-level data and proposed several measures to evaluate the size of the meMO effect explained by moderator variables in total, directly, or indirectly. The performance of the 2meMO model is compared against that of the traditional meMO model via Monte Carlo simulations. Results indicate that, when the assumption of homoscedasticity holds, 2meMO yields comparable results with those under meMO. When the homoscedasticity assumption is violated, estimates under 2meMO are more accurate than those under meMO. More importantly, the measures of the size of the meMO effect proposed in this article can be used as a supplement to the test of meMO effects and will meet the needs for reporting ES in practice. Consequently, the 2meMO model is recommended for the analysis of mediated moderation, and the effect sizes (ESs) for the interpretation of the effect according to the questions of interest are better reported.
Subjects: Psychology >> Social Psychology submitted time 2023-03-27 Cooperative journals: 《心理学报》
Abstract： Assessment datasets contaminated by non-effortful responses may lead to serious consequences if not handled appropriately. Previous research has proposed two different strategies: down-weighting and accommodating. Down-weighting tries to limit the influence of aberrant responses on parameter estimation by reducing their weight. The extreme form of down-weighting is the detection and removal of irregular responses and response times (RTs). The standard residual-based methods, including the recently developed residual method using an iterative purification process, can be used to detect non-effortful responses in the framework of down-weighting. In accommodating, on the other hand, one tries to extend a model in order to account for the contaminations directly. This boils down to a mixture hierarchical model (MHM) for responses and RTs. However, to the authors’ knowledge, few studies have compared standard residual methods and MHM under different simulation conditions. It is unknown which method should be applied in different situations. Meanwhile, MHM has strong assumptions for different types of responses. It would be valuable to examine the performance of the method when the assumptions are violated. The purpose of this study is to compare standard residual methods and MHM under a fully crossed simulation design. In addition, specific recommendations for their applications are provided. The simulation study included two scenarios. In simulation scenario I, data were generated under the assumptions of MHM. In simulation scenario II, the assumptions of MHM concerning non-effortful responses and RTs were both violated. Simulation scenario I had three manipulated factors. (1) Non-effort prevalence (ππ\pi ), which was the proportion of individuals with non-effortful responses. It had three levels: 0%, 20% and 40%. (2) Non-effort severity (πnoniπinon\pi _{i}^{non}), which was the proportion of non-effortful responses for each non-effortful individual. It varied between two levels: low and high. When πnoniπinon\pi _{i}^{non} was low, πnoniπinon\pi _{i}^{non} was generated from U (0, 0.25); while when πnoniπinon\pi _{i}^{non} was high, πnoniπinon\pi _{i}^{non} was generated from U (0.5, 0.75), where “U” denoted a uniform distribution. (3) Difference between RTs of non-effortful and effortful responses (dRTdRT{{d}_{RT}}). The difference between RTs from two groups, dRTdRT{{d}_{RT}}, had two levels, small and large. The logarithm of RTs of non-effortful responses were generated from normal distribution N (μμ\mu ,0.50.50.52), where μ =−1 μ =−1\text{ }\!\!\mu\!\!\text{ }=-1 when dRTdRT{{d}_{RT}} was small, μ =−2 μ =−2\text{ }\!\!\mu\!\!\text{ }=-2 when dRTdRT{{d}_{RT}} was large. For generating the non-effortful responses, we followed Wang, Xu and Shang (2018), with the probability of a correct response gjgj{{g}_{j}} setting at 0.25 for all non-effortful responses. In simulation scenario II, only the first two factors were considered. Non-effortful RTs were generated from a uniform distribution with a lower bound of exp(−5)exp(−5)\text{exp}\left( -5 \right) and upper bound being the 5th percentile of RT on item j with τ=0τ=0\tau =0. The probability of a correct response for non-effortful responses was dependent on the ability level of each examinee. In all the conditions, sample size was fixed at I = 2,000 and test length was fixed at J = 30. For each condition, 30 replications were generated. For effortful responses, Responses and RTs were simulated from van der Linden’s (2007) hierarchical model. Item parameters were generated with aj ~U(1,2.5)aj ~U(1,2.5){{a}_{j}}\tilde{\ }U\left( 1,2.5 \right), bj ~N(0,1)bj ~N(0,1){{b}_{j}}\tilde{\ }N\left( 0,1 \right), αj ~U(1.5,2.5),βj ~U(−0.2,0.2) αj ~U(1.5,2.5),βj ~U(−0.2,0.2)~{{\alpha }_{j}}\tilde{\ }U\left( 1.5,2.5 \right),{{\beta }_{j}}\tilde{\ }U\left( -0.2,0.2 \right). For simulees, the person parameters (θi,τi)(θi,τi)\left( {{\theta }_{i}},{{\tau }_{i}} \right) were generated from a bivariate normal distribution with the mean vector of μ=(0,0)′μ=(0,0)′\mathbf{\mu }=\left( 0,0 \right)'and the covariance matrix of Σ=[10.250.250.25]Σ=[10.250.250.25]\mathbf{\Sigma }=\left[ \begin{matrix} 1 & 0.25 \\ 0.25 & 0.25 \\ \end{matrix} \right]. Four methods were compared under each condition: the original standard residual method (OSR), conditional estimate standard residual (CSR), conditional estimate with fixed item parameters standard residual method using iterative purifying procedure (CSRI), and MHM. These methods were implemented in R and JAGS using a Bayesian MCMC sampling method for parameter calibration. Finally, these methods were evaluated in terms of convergence rate, detection accuracy and parameter recovery. The results are presented as following. First of all, MHM suffered from convergence issues, especially for the latent variable indicating non-effortful responses. On the contrary, all the standard residual methods achieved convergence successfully. The convergence issues were more serious in simulation scenario II. Secondly, when all the items were assumed to have effortful responses, the false positive rate (FPR) of MHM was 0. Although the standard residual methods had FPR around 5% (the nominal level), the accuracy of parameter estimates was similar for all these methods. Third, when data were contaminated by non-effortful responses, CSRI had higher true positive rate (TPR) almost in all the conditions. MHM showed lower TPR but lower false discovery rate (FDR), exhibiting even lower TPR in simulation scenario II. When πnoniπinon\pi _{i}^{non} was high, CSRI and MHM showed more advantages over the other methods in terms of parameter recovery. However, when πnoniπinon\pi _{i}^{non} was high and dRTdRT{{d}_{RT}} was small, MHM generally had higher RMSE than CSRI. Compared to simulation scenario I, MHM performed worse in simulation scenario II. The only problem CSRI needed to deal with was its overestimation of time discrimination parameter across all the conditions except for when ππ\pi =40% and dRTdRT{{d}_{RT}} was large. In a real data example, all the methods were applied to a dataset collected for program assessment and accountability purposes from undergraduates at a mid-sized southeastern university in USA. Evidences from convergence validity showed that CSRI and MHM might detect non-effortful responses more accurately and obtain more precise parameter estimates for this data. In conclusion, CSRI generally performed better than the other methods across all the conditions. It is highly recommended to use this method in practice because: (1) It showed acceptable FPR and fairly accurate parameter estimates even when all responses were effortful; (2) It was free of strong assumptions, which meant that it would be robust under various situations; (3) It showed most advantages when πnoniπinon\pi _{i}^{non} was high in terms of the detection of non-effortful responses and the improvement of the parameter estimation. In order to improve the estimation of time discrimination parameter in CSRI, the robust estimation methods that down-weight flagged response patterns can be used as an alternative to directly removing non-effortful responses (i.e., the method in the current study). MHM can perform well when all its assumptions are met and πnoniπinon\pi _{i}^{non} is high, dRTdRT{{d}_{RT}} is large. However, some parameters have difficulty in convergence under MHM, which will limit its application in practice.
Subjects: Psychology >> Social Psychology submitted time 2023-03-27 Cooperative journals: 《心理学报》
Abstract： As a new form of test, cognitive diagnostic assessment has attracted wide attention from researchers at home and abroad. At the same time, missing data caused by characteristics of the test design is a rather common issue encountered in cognitive diagnostic tests. It is therefore very important to develop an effective solution for dealing with missing data in cognitive diagnostic assessment ensuring that diagnosis feedback provided to both students and teachers is more accurate and reliable. As a matter of fact, machine learning has been applied to impute missing data in recent years. As one of the machine learning algorithms, the random forest has been proved to be a state-of-the-art learner because it exhibits good performance when handling classification and regression tasks with effectiveness and efficiency, and is capable of solving multi-class classification problems in an efficient manner. Interestingly, this algorithm has a distinct advantage in terms of coping with noise interference. Furthermore, the random forest imputation method, an improved algorithm for dealing with missing data based on the random forest algorithm, makes full use of the available response information and characteristics of response patterns of participants to impute missing data instead of assuming the mechanism of missingness in advance. By combining the advantages of the random forest method in classification and prediction and the assumption-free feature of the random forest imputation method, we attempt to improve the existing random forest imputation algorithm so that the method can be properly applied to handle missing data in cognitive diagnostic assessment. On the basis of the DINA (Deterministic Inputs, Noise "And" Gate) model, widely used in cognitive diagnostic assessment, we introduce the RCI (Response Conformity Index) into missing data imputation to identify threshold of imputation type and hence proposes a new method for handling missing responses in the DINA model: random forest threshold imputation (RFTI) approach. Two simulation studies have been conducted in order to validate the effectiveness of RFTI. In addition, the advantages of the new method have been explored by comparing it with traditional techniques for handling missing data. First, the theoretical basis and algorithm implementation of RFTI were described in detail. Then, two Monte Carlo simulations were employed to validate the effectiveness of RFTI in terms of imputation rate and accuracy as well as the accuracy in DINA model parameter estimation. Moreover, the applicability of RFTI was investigated by considering different mechanisms for missingness (MNAR, MIXED, MAR and MCAR) and different proportions of missing values (10%, 20% 30%, 40% and 50%). The main results indicated: (1) imputation accuracy of RFT was significantly higher than that of the random forest imputation (RFTI) methods, and the data missingness rate treated by RFTI was about 10% under all conditions; (2) the highest attribute pattern match ratio and attribute marginal match ratio of participants were observed using RFTI under all conditions as compared to that of EM algorithm and RFI. Moreover, this behavior depended on the proportion and mechanisms of missing data. Results indicated that this phenomenon became more obvious when the missingness mechanism was MNAR and MIXED and the proportion of missing responses were more than 30%. However, the new algorithm failed to show superiority in estimating DINA model parameter. Based on these results, we conclude the article with an overall summary and recommendations, as well as the further direction.
Subjects: Psychology >> Social Psychology submitted time 2023-03-27 Cooperative journals: 《心理学报》
Abstract： Humans are a social species that are constantly involved in complex relationships, reacting to the actions of others, and intentionally or unintentionally changing our own behavior. Personality traits reflect the behavioral pattern of an individual's response to the environment, which also includes social behavior. In addition, the brain is also an important factor when discussing social networks. The brain provides biological mechanisms for human behavior, while social networks provide external triggers for these behaviors. Linking personality traits and brain activity to social networks can help us better understand the structure of group relationships, improve our understanding of individual human beings, and help us better predict individual social behaviors and find the rules of information transmission in interpersonal relationships. From the perspective of a network, we collected nine social networks from 94 undergraduate students in the same grade according to their different social needs. We used the graph theory and resting-state functional magnetic resonance imaging to explore the influence of personality traits on social networks based on individual popularity and closeness between individuals and the relationship between the similarity of brain resting-state functional connectivity and social distance between individuals. Specifically, regression analysis was carried out, with personality traits as the independent variables and the different degrees of social networks as the dependent variables. Then, a correlation analysis was performed for the social distance and similarity of personality traits. Finally, the correlation between the similarity of the brain networks and social distance was calculated. The results showed that (1) individuals with high conscientiousness were more popular in social networks requiring "trust" traits, while individuals with high agreeableness were more popular in social networks requiring "fun" traits. These findings showed that in the same group, there are different social networks according to social needs, and the popularity of individuals in different social networks is not similar as it will be affected by the corresponding personality traits; (2) In the social networks requiring "shared interests & values, " personality similarity and social distance between individuals were significantly negatively correlated. Personality similarity promotes interpersonal communication between individuals, which may be realized through interpersonal attraction induced by the similarity of values and interests; (3) In the same social network, there is a significantly negative correlation between similarities in functional connections (FCs) and social distance among individuals, and these FCs are mainly concentrated in the fronto-parietal task control network and the dorsal attention network. The similarity of resting brain FCs among individuals may promote interpersonal communication, possibly due to the similarity of individuals in cognitive control and environmental processing bias, which increases the interpersonal attraction and shortening the social distance between individuals. The results revealed the influence of personality traits on the structure of different social networks, the relationship between personality trait similarity among individuals, and the similarity between resting brain networks and social distance, which has important implications for understanding the structure of social networks, the formation rules, and the information transmission rules among them. In addition, this study discussed the relationship between the similarity of resting-state FC and social distance, providing new evidence for studies on brain synchronization in interpersonal communication and brain imaging evidence for the study of the relationship between the similarity of personality traits and social distance.#social networks, personality traits, resting-state functional connectivity
Subjects: Psychology >> Social Psychology submitted time 2023-03-27 Cooperative journals: 《心理学报》
Abstract： Secure and stable parent-child attachment is beneficial for children’s mental health, whereas maladaptive parent-child attachment may lead to children’s maladjustment such as depressive symptoms. Due to rapid sociocultural development in China, traditional patriarchal parenting patterns with fathers as breadwinners and mothers as homemakers have gradually diminished. Instead, Chinese fathers today spend an increasing amount of involvement in their children’s development. However, little to no research has examined the unique effects of father-child and mother-child attachment on children’s developmental outcomes in contemporary China. More importantly, the Biological Sensitivity to Context Theory (BSCT) suggests that the association between parent- child attachment and child developmental outcomes may vary among children with different levels of biological sensitivity. According to this theory, children who are biologically sensitive are more susceptible to adaptive or maladaptive parent-child relationships. Taken together, the present study aimed to examine how children’s depressive symptoms were affected by mother-child and father-child attachments and whether this effect was moderated by the children’s biological sensitivity. The potential father vs. mother difference was also examined. 150 school-aged children (63 girls and 87 boys, Mage = 8.64 years) participated in the current study. Children reported their depressive symptoms as well as their perceived parent-child attachment with mothers and fathers, respectively. Children’s biological sensitivity (i.e., vagal suppression) was assessed by the decrease of respiratory sinus arrhythmia (RSA) between the resting phase and the task phases (i.e., a social stress task and a negative emotion provoking task) through the Biopac MP150 systems. Descriptive statistics and bivariate correlations were analyzed via SPSS 25.0, and moderation models were conducted via Mplus 8.3. Our results are highlighted by five major points: (1) The level of mother-child attachment was higher than that of father-child attachment. (2) Both mother-child attachment and father-child attachment were uniquely and negatively associated with children's depressive symptoms, and the strengths of the aforementioned paths were equivalent. (3) Children's biological sensitivity (vagal suppression) measured in the social stress task and the negative emotion provoking task had similar moderating effects on the relationship between parent-child attachment and children’s depressive symptoms, indicating the cross-context consistency of the roles of biological sensitivity. (4) Children’s cross-context biological sensitivity moderated the relationship between mother-child attachment and children’s depressive symptoms. Specifically, highly sensitive children (vs. non-sensitive children) were more likely to benefit from secure mother-child attachment but were also more likely to be harmed by insecure mother-child attachment. (5) Children's cross-context biological sensitivity did not moderate the relationship between father-child attachment and children’s depressive symptoms, such that higher father-child attachment was consistently associated with lower children’s depressive symptoms, regardless of children’s levels of biological sensitivity. Based on attachment theory and the BSCT, the present study indicates that children’s attachment with their mothers or fathers are uniquely associated with children's depressive symptoms, and that mother-child attachment jointly interacted with children’s biological sensitivity to influence children’s depressive symptoms. As a theoretical application, our study innovatively suggests that future studies should consider the context in which an indicator of biological sensitivity is assessed as well as parental roles (father vs. mother) when testing the BSCT in family studies. As a practical application, our findings indicate the potential different roles of father-child attachment and mother-child attachment in protecting children from suffering depressive symptoms, providing empirical evidence to support the development of family-based prevention and intervention projects aimed at alleviating children’s psychopathological problems.
Subjects: Psychology >> Cognitive Psychology submitted time 2018-09-07 Cooperative journals: 《心理学报》
Abstract： 工作记忆训练的训练获益能够迁移至与工作记忆相关的各项基础认知活动中去。基于情绪认知控制与工作记忆之间的密切关联, 本研究设计了一种新型情绪性双维n-back训练, 并且验证利用手机APP搭载训练任务的适用性。结果表明：基于APP的短期双维n-back训练能够使个体在视空间工作记忆任务、活动记忆任务、数字转换任务、Stroop任务上的成绩产生相较控制组更大的进步, 表明训练可以提高个体工作记忆的容量和中央执行功能。然而, 基于不同情绪材料的训练(中性、负性、正性)在各类迁移上差异不大。短期训练无法迁移到情绪Stroop任务中, 即无法产生特异的情绪控制上的效益。双维n-back任务通道单一, 且搭载于手机APP, 拥有较为广阔的应用前景。但将情绪材料单纯地糅合进工作记忆任务中的价值与意义须被进一步考察。
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