Subjects: Psychology >> Developmental Psychology submitted time 2024-04-09
Abstract： Anxiety disorders reach their peak prevalence during adolescence, significantly impacting young individuals’ physical and mental health. Current insights into the pathogenesis, evolution, and treatment of adolescent anxiety predominantly focus on fear processing at a conscious level, overlooking a crucial aspect: the prefrontal cortex and its top-down control functions are not yet fully developed in adolescents. Therefore, applying a top-down mechanism to clinical treatment for adolescents may have limitations. Moreover, exploring automatic fear processing may help to extend the knowledge about the pathogenesis of anxiety in adolescents. This is the first research combined with unconscious perception to explore the occurrence, development, and mechanism of anxiety in adolescents. Recruiting adolescents who are in anxiety or vulnerable to anxiety as subjects and integrating paradigms used for examining unconsciousness, we aim to explore: 1) the occurrence and development of unconscious fear processing, along with its underlying neural mechanisms in adolescents, and the impact of chronic stress hormones; 2) the role of unconscious fear processing in the development of anxiety in adolescents; 3) the noninvasive intervention for unconscious fear in adolescents.This project will provide scientific support for the prevention, recognition, and intervention of anxiety in adolescents and to promote all-round development of adolescents in physical and mental.
Subjects: Psychology >> Cognitive Psychology submitted time 2023-11-06
Abstract： Visual working memory (VWM) plays a foundational role in advanced cognitive functions. The state-based models propose a hierarchical organization of functional states, where memory representations with high attentional priority are retained in an online state (i.e., active state), while those with lower priority are kept in an offline state (i.e., passive state) for later use. The memory representations can be dynamically transferred between the two states according to the task demands. However, there was rare work to explore how the memory representations transitioned into the offline state from the online, generating the offline representations. Here, we put forward two hypothesis, the consolidation hypothesis and the fade-away hypothesis.
To explore this question, participants were instructed to remember two sequential memory arrays, with Memory array 2 being detected before Memory array 1. In this memory task, Memory array 1 was held in the offline state during the active maintenance of Memory array 2. Colored squares served as memory stimuli. 30 healthy college students participated in each experiment. We primarily modulated the temporal context related to the state transformation of memory representations: the interval delay between the two memory arrays in Experiment 1 and the presentation time of Memory array 2 in Experiment 2. The load of online memory varied between two and four in each trial. These variables were within-subject factors. Experiment 1 aimed to verify that the shortage of interval delay between memory arrays led to the failure of state transformation in the condition of 0.8s-interval. Experiment 2 attempted to determine which hypothesis, consolidation or fade-away, aligned better with the state transformation process.
The exploration of representational state transformation was built on the resources-dissociation account, which proposed that the offline representations are independent of the active processing of online representations. Memory arrays 1 and 2 were used to test the offline and online memory, respectively. The results of Experiment 1 showed that variations in online load did not affect offline memory when extending the interval delay from 0.8s to 1s. This indicated that the state transformation of Memory array 1 continued beyond 0.8s after its disappearance and could complete within a 1s-interval. In Experiment 2, the interval was designed at 0.8s. We observed that the online load variation had no impact on offline memory when extending the presentation time of Memory array 2 from 0.2s to 0.5s. This supported the consolidation hypothesis, indicating that the sufficient presentation time of Memory array 2 allowed for the state transformation of Memory array 1 to complete before the subsequent processing of Memory array 2. Thus, we concluded that the state transformation involved a consolidation processing to transfer the online representations to the offline state, rather than natural fade-away of persistent neural activity.
In summary, the state transformation acts as a process of consolidating online memory representations into the offline state, thereby forming offline representations. This process can be completed within a sufficiently long retention interval, or continue during the presentation of subsequent stimuli when providing a deficient interval. The current findings provide fresh insights into the mechanisms of representational maintenance in the two distinct states.
Subjects: Psychology >> Statistics in Psychology submitted time 2023-10-07
Abstract： Hierarchical data, which is observed frequently in psychological experiments, is usually analyzed with the linear mixed-effects models (LMEMs), as it can account for multiple sources of random effects due to participants, items, and/or predictors simultaneously. However, it is still unclear of how to determine the sample size and number of trials in LMEMs. In history, sample size planning was conducted based purely on power analysis. Later, the influential article of Maxwell et al. (2008) has made clear that sample size planning should consider statistical power and accuracy in parameter estimation (AIPE) simultaneously. In this paper, we derive a confidence interval width contours plot with the codes to generate it, providing power and AIPE information simultaneously. With this plot, sample size requirements in LMEMs based on power and AIPE criteria can be decided. We also demonstrated how to run sensitivity analysis to assess the impact of the magnitude of experiment effect size and the magnitude of random slope variance on statistical power, AIPE and the results of sample size planning.
There were two sets of sensitivity analysis based on different LMEMs. Sensitivity analysis Ⅰ investigated how the experiment effect size influenced power, AIPE and the requirement of sample size for within-subject experiment design, while sensitivity analysis Ⅱ investigated the impact of random slope variance on optimal sample size based on power and AIPE analysis for the cross-level interaction effect. The results for binary and continuous between-subject variables were compared. In these sensitivity analysis, two factors regarding sample size varied: number of subjects (I=10, 30, 50, 70, 100, 200, 400, 600, 800), number of trials (J=10, 20, 30, 50, 70, 100, 150, 200, 250, 300). The additional manipulated factor was the effect size of experiment effect (standard coefficient of experiment condition= 0.2, 0.5, 0.8, in sensitivity analysis Ⅰ) and the magnitude of random slope variance (0.01, 0.09 and 0.25, in sensitivity analysis Ⅱ). A random slope model was used in sensitivity analysis Ⅰ, while a random slope model with level-2 independent variable was used in sensitivity analysis Ⅱ. Data-generating model and fitted model were the same. Estimation performance was evaluated in terms of convergence rate, power, AIPE for the fixed effect, AIPE for the standard error of the fixed effect, and AIPE for the random effect.
The results are as following. First, there were no convergence problems under all the conditions , except that when the variance of random slope was small and a maximal model was used to fit the data. Second, power increased as sample size, number of trials or effect size increased. However, the number of trials played a key role for the power of within-subject effect, while sample size was more important for the power of cross-level effect. Power was larger for continuous between-subject variable than for binary between-subject variable. Third, although the fixed effect was accurately estimated under all the simulation conditions, the width 95% confidence interval (95%width) was extremely large under some conditions. Lastly, AIPE for the random effect increased as sample size and/or number of trials increased. The variance of residual was estimated accurately. As the variance of random slope increased, the accuracy of the estimates of variances of random intercept decreased, and the accuracy of the estimates of random slope increased.
In conclusion, if sample size planning was conducted solely based on power analysis, the chosen sample size might not be large enough to obtain accurate estimates of effects size. Therefore, the rational for considering statistical power and AIPE during sample size planning was adopted. To shed light on this issue, this article provided a standard procedure based on a confidence interval width contours plot to recommend sample size and number of trials for using LMEMs. This plot visualizes the combined effect of sample size and number of trials per participant on 95% width, power and AIPE for random effects. Based on this tool and other empirical considerations, practitioners can make informed choices about how many participants to test, and how many trials to test each one for.
Subjects: Psychology >> Other Disciplines of Psychology submitted time 2022-06-09
Abstract：
The death number caused by suicide is increasing, but the psychological mechanism of suicide is not clear. Recent studies have found that disgust might be the major emotional factor leading to suicide, or the reason for people to commit suicide is due to self-disgust. Disgust is a kind of basic emotion that is induced by excreta from oneself or other people. Disgust works to keep the individual away from toxicity and disease, which can be called "immune behavior". Recently, it is found that many psychological problems are resulted from the disgust reactions to surrounding people or environment. Some studies have shown that individuals who commit suicide are very disgustful for themselves, indicating that their disgust emotion is problematic. Just as somatic autoimmunity attacks itself, disgust might lead to attacking behaviors against oneself or suicide. Self-disgust is the key factor of suicidal ideation, and early trauma is the root cause. Life stress and mental illness are involved in that disgust induced suicidal ideation. The neural basis of suicide induced by disgust is related to HPA axis and serotonin system. Future studies might apply neuroscience technologies, such as neuroimaging and electrophysiology, to examine the neural mechanism of suicidal behavior, and to explore the psychological and neural mechanisms of disgust affecting suicidal behaviors.
Peer Review Status:
Awaiting Review
Subjects: Psychology >> Cognitive Psychology Subjects: Psychology >> Experimental Psychology submitted time 2022-06-07
Abstract：
Contextual cueing refers to the global properties of a context or scene used to search for specific objects and regions. Chun and Jiang (1998) found that in a visual search, the reaction time to repeated configurations was shorter than the reaction time to newly generated configurations. The benefit of repeated context–target association is widely known as the contextual-cueing effect, which indicates that the subject has learned the contextual association by which attention is guided to facilitate the searching. However, the learning of contextual cueing lacks adaptability. When the subject has learned a set of contexts, it is difficult to update a new target into existing contexts (re-learning) or to learn a new set of contexts (new-learning). Previous studies have shown that restarted learning processes can facilitate the learning of new context–target associations, while updating old contexts is associated with the scope of attention. Notably, positive emotions could broaden the scope of attention and break the cognitive fixation on old processes; therefore, it is possible to improve the adaptability of contextual-cueing learning via positive emotions.
This study aimed to explore whether positive emotions could enhance the adaptability of contextual learning. To this end, we recruited a sample of 18 young adults with positive and neutral affective priming as experimental conditions and control conditions, respectively, which allowed us to explore the contextual-cueing effect under the conditions of re-learning and new-learning. It should be noted that contextual cueing was defined in operation as the reaction time to the newly generated configuration minus that to the repeated configuration.
The experiment was divided into two phases: the learning phase and the switch phase. In the learning phase, the subjects learned a set of contextual cues. In the switch phase, with the contextual-cueing effect as the dependent variable, a repeated measures ANOVA was conducted with the emotional valence (positive versus neutral), the new contextual-cueing learning type (re-learning versus new-learning), and the time phase (early phase versus late phase).
The results indicated that neutral emotions did not facilitate contextual-cueing learning irrespective of the new contextual-cueing learning type. However, positive emotion improved learning in the new-learning condition, in which the contextual-cueing effect was higher in positive emotions than in neutral emotions both in the late phase and the early phase, whereas the re-learning condition did not show any sign of a contextual-cueing effect above zero.
This study indicates that positive emotions can improve the adaptability of contextual-cueing learning and that the underlying mechanism restarts learning processing, which fails to prevent an automatic retrieval of the old presentations caused by similarity. Therefore, it facilitates the learning of new contextual cueing but does not update learned contextual cueing.
Peer Review Status:Awaiting Review
Subjects: Psychology >> Cognitive Psychology submitted time 2022-02-12
Abstract：
How negative affects influences conflict adaptation has been widely studied and of a concern for researchers. According to the types of negative affect that is either incidental or integral, this research question can be further discussed from the separated and the integrated relationship of cognition with emotion. From the separated perspective, incidental negative affect, which is manipulated externally from conflict, is an independent factor that modulates conflict adaptation by means of activating emotional processing system, alternatively by mediating individuals’ arousal/motivational levels. Interestingly, recent studies have indicated that conflict is inherently aversive and being termed as integral negative affect, thus suggesting the inherent relationship between conflict and negative affect. In this sense, negative affect can be regarded as another source that plays the similar role with conflicting information in generating conflict adaptation. Accordingly, from the integrated perspective, integral negative affect is highly integrated with conflict processing, which can inherently promote goal-related performance and elicit conflict adaptation. Therefore, discussing the influence of negative affect on conflict adaptation from the insight into the relationship of cognition (conflict) with negative affect deepens our understanding regarding how negative affect exerts its impact on conflict adaptation, which also provides a new insight into how cognition integrates with emotion. On this basis, we further put forward some new directions for the future studies.
Peer Review Status:Awaiting Review
Subjects: Psychology >> Statistics in Psychology submitted time 2021-11-29
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 (π), which was the proportion of individuals with non-effortful responses. It had three levels: 0%, 20% and 40%. (2) Non-effort severity (π_i^non), which was the proportion of non-effortful responses for each non-effortful individual. It varied between two levels: low and high. When π_i^non was low, π_i^non was generated from U (0, 0.25); while when π_i^non was high, π_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 (d_RT). The difference between RTs from two groups, d_RT, had two levels, small and large. The logarithm of RTs of non-effortful responses were generated from normal distribution N (μ,0.52), where μ=-1 when d_RT was small, μ=-2 when d_RT was large. For generating the non-effortful responses, we followed Wang, Xu and Shang (2018), with the probability of a correct response 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) and upper bound being the 5th percentile of RT on item j with τ=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 a_j~U(1,2.5), b_j~N(0,1), 〖 α〗_j~U(1.5,2.5)， β_j~U(-0.2,0.2). For simulees, the person parameters (θ_i,τ_i) were generated from a bivariate normal distribution with the mean vector of μ=(0,0)'and the covariance matrix of Σ=[■(1&0.25@0.25&0.25)]. 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 π_i^non was high, CSRI and MHM showed more advantages over the other methods in terms of parameter recovery. However, when π_i^non was high and 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 π=40% and 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 π_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 π_i^non is high, d_RT is large. However, some parameters have difficulty in convergence under MHM, which will limit its application in practice.
Peer Review Status:Awaiting Review
Subjects: Psychology >> Physiological Psychology submitted time 2021-07-27
Abstract： Disgust is an important basic emotion for human beings and animals, and it derives from distasteful oral responses to bitter (toxic) tastants, often accompanied with nausea and vomiting and a strong desire to stay away from the induced stimulus, and it has the function of avoiding potential disease threats. A large number of studies have shown that oxytocin, progesterone, and estrogens can affect perception, generation and expression of core disgust, as well as learning conditioned disgust and recognizing facial expression in varying degrees. These three hormones mainly affect the processing of disgust by modulating neurotransmitter receptors including serotonin, γ-aminobutyric acid, acetylcholine, and glutamic acid receptors, and thus affecting the activities of amygdala, insula, anterior cingulate gyrus, putamen, piriform cortex, and middle frontal gyrus. Future studies should explore the effects of these hormones on disgust in different sensory channels and also consider their moderating roles in different genders by accurately measuring hormone levels and controlling the task difficulties. In addition, researchers can combine neuroimaging technologies with behavioral studies to clarify the neuroendocrine mechanism of these hormones affecting disgust processing.
Subjects: Psychology >> Psychological Measurement submitted time 2021-05-08
Abstract： The mixture model method (MMM) is a new method proposed to handle data contaminated by aberrant responses in psychological and educational measurement. Compared to the traditional response time threshold methods and the response time residual methods, MMM shows the following advantages: (1) MMM detects aberrant responses and obtaining parameter estimates simultaneously; (2) it precisely recovers the severity of aberrant responding. Through building different item response models and response time models for different latent groups, MMM helps to identify aberrant responses from normal responses. Future researches could investigate the performance of MMM when its assumptions are violated or using data with other types of aberrant response patterns. The computation efficiency of MMM is also likely to be improved by fixing part of the item parameter estimates or by using an optimal way of choosing suitable methods.
Peer Review Status:Awaiting Review
Subjects: Psychology >> Physiological Psychology submitted time 2019-11-22
Abstract： Infra-slow oscillation (ISO) is a kind of brain rhythm between 0.01 and 0.1 Hz. ISO is widely distributed in multiple brain regions. As an important brain activity, the ISO interacts with high-frequency neural rhythm via cross-frequency coupling while has different activity patterns from high-frequency neural activity. ISO may be generated by the dynamic activity of thalamus, glia and ions, regulating the overall excitability of the brain and thereby affecting the efficiency of cognitive activities. The frequency, amplitude, and phase of ISO could all regulate the overall efficiency of cognitive activity. Future researches should investigate the relationship between various physiological mechanisms of ISO and diverse cognitive activities, and explore the rules of the interaction between ISO and mental activities, further promoting the construction of rhythmic theory of brain function. "
Peer Review Status:Awaiting Review
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