Percentile Norms and Accompanying Interval Estimates from an Australian General Adult Population Sample for Self‐Report Mood Scales (BAI,BDI, CRSD,CES‐D,DASS, DASS‐21, STAI‐X,STAI‐Y,SRDS, and SRAS)

Despite their widespread use, many self‐report mood scales have very limited normative data. To rectify this, Crawford et al. have recently provided percentile norms for a series of self‐report scales. The present study aimed to extend the work of Crawford et al. by providing percentile norms for additional mood scales based on samples drawn from the general Australian adult population. Participants completed a series of self‐report mood scales. The resultant normative data were incorporated into a computer programme that provides point and interval estimates of the percentile ranks corresponding to raw scores for each of the scales. The programme can be used to obtain point and interval estimates of the percentile ranks of an individual's raw scores on the Beck Anxiety Inventory, the Beck Depression Inventory, the Carroll Rating Scale for Depression, the Centre for Epidemiological Studies Rating Scale for Depression, the Depression, Anxiety, and Stress Scales (DASS), the short‐form version of the DASS (DASS‐21), the Self‐rating Scale for Anxiety, the Self‐rating Scale for Depression, the State–Trait Anxiety Inventory (STAI), form X, and the STAI, form Y, based on normative sample sizes ranging from 497 to 769. The interval estimates can be obtained using either classical or Bayesian methods as preferred. The programme (which can be downloaded at http://www.abdn.ac.uk/~psy086/dept/MoodScore_Aus.htm ) provides a convenient and reliable means of obtaining the percentile ranks of individuals' raw scores on self‐report mood scales.     

Sequential dependencies in driving

The effect of recent experience on current behavior has been studied extensively in simple laboratory tasks. We explore the nature of sequential effects in the more naturalistic setting of automobile driving. Driving is a safety-critical task in which delayed response times may have severe consequences. Using a realistic driving simulator, we find significant sequential effects in pedal-press response times that depend on the history of recent stimuli and responses. Response times are slowed up to 100 ms in particular cases, a delay that has dangerous practical consequences. Further, we observe a significant number of history-related pedal misapplications, which have recently been noted as a cause for concern in the automotive safety community. By anticipating these consequences of sequential context, driver assistance systems could mitigate the effects of performance degradations and thus critically improve driver safety.     

性别二态线索对男性面孔吸引力影响的方法差异之争

性别二态线索(男性化-女性化线索)对男性面孔吸引力的影响一直存在不一致的结果,研究者主要从方法差异对此不一致性进行解释.本文首先对操作男性化面孔刺激的不同计算机图像处理技术进行了阐述,主要包括性别二态技术、感知男性化技术和青春期发展技术,并进一步梳理了以往采用不同技术进行比较的相关研究,最后对未来研究提出了展望.     

Emotion development system by interacting with human EEG and natural scene understanding

In this paper, we propose a mental development system for understanding the emotional status of humans, and sharing emotions with human subjects. According to the relationship between emotional factors and characteristics of an image, we incorporate the fuzzy concept to extract emotional features using L^*C^*H^* color and orientation information. On the other hand, we also consider the EEG signals which are stimulated by natural stimuli to form the semantic emotional features as well. Emotionally relevant features are firstly clustered into two categories with degrees of belongingness to each cluster to initialize the membership functions of a neuro-fuzzy system. The IF-THEN rules of a neuro-fuzzy system to understand the positive and negative human emotions will be constructed by interacting with human. Then the system attempts to extend the number of understandable emotion. Through the time, the system sub-clusters the emotional features so that the number of membership function of the neuro-fuzzy network will increase to incorporate more complicated human expertise considering more human emotions. Using such a developmental process, the proposed system can develop a mental ability to understand more complex human emotions by mining the characteristics of emotional features and interacting with its environment.     

Christian Prayer and the Local Congregation: A Social Analysis of Individual Prayer Petitions

Abstract

Prayer requests from members and attendees of a progressive Christian church located in a large American City was the subject of this investigation. This church serves congregants from Roman Catholic and Protestant backgrounds, and a large, but not exclusively gay, lesbian, bisexual, transgender community. ap Siôn’s study of prayer typology and subject contents from rural England influenced this investigation. This study used computer technology and document analysis to categorize 8,059 individual prayer card requests submitted between 2014-2018 from church members and attendees. Results showed the most common prayer requests were “Thanks and Thanksgiving” prayers with “Praise and Adoration” prayers among the least common. Few references to sin and forgiveness were found. The most frequently mentioned subjects of prayers were those referencing personal needs and concerns. Findings and comparison with ap Siôn and other research suggest these prayer requests demonstrate the social and theological cohesiveness of Christian prayer. Additional research is suggested comparing Christian prayer with prayers in other religions and how prayer behaviors interact with the rise of multiculturalism in society.     

Decreasing Adolescent Drug Use and School Failure:

Behavioral analyses of 3 adolescents show that 3 months of targeted family problem-solving training can decrease drug use and school failure by the end of a 1 1/4 year follow-up while control behaviors remain stable. Some findings, however, reflected incomplete understanding of the controlling variables: (a) lengthy delays before behavioral improvement, (b) recurrences of the problem behavior and subsequent recoveries during follow-up, and (c) correlated changes in the home and school environments. It is suggested that systematic study of relevant variables in the intervention could reduce behavioral variability and further increase understanding of adolescent drug use.     

Factor structure of the CNS Vital Signs computerized cognitive battery in youth with neurological diagnoses

Computerized cognitive batteries, such as CNS Vital Signs (CNSVS), can provide valuable information in clinical and research settings. However, psychometric properties, especially in children and adolescents, remain relatively understudied. The aim of this study was to investigate the factor structure of CNSVS in children and adolescents with neurological diagnoses.

Participants with neurological diagnoses (N = 280) age 7–19 years were assessed as part of their clinical care at a tertiary hospital. All participants received the full CNSVS computerized cognitive battery, which contains seven subtests designed to measure attention, executive functioning, psychomotor speed, and memory. Principal components analyses were used to examine factor structure.

Scores from CNSVS subtests loaded onto a three-component solution and accounted for 46% of the variance. The three components were deemed to best represent (1) speed, (2) memory, and (3) inhibition, with subtest scores loading differently than the original 11 primary and secondary domain scores would have suggested.

Although the CNSVS program generates numerous primary and secondary domain scores, a three-component solution represents a more parsimonious approach to interpreting performance on the CNSVS in youth with neurological diagnoses. Confirmation of this factor solution in other samples is warranted.     

Assessment of generalised Bayesian structural equation models for continuous and binary data

The paper proposes a novel model assessment paradigm aiming to address shortcoming of posterior predictive $p$-values, which provide the default metric of fit for Bayesian structural equation modelling (BSEM). The model framework presented in the paper focuses on the approximate zero approach (Psychological Methods, 17 , 2012, 313), which involves formulating certain parameters (such as factor loadings) to be approximately zero through the use of informative priors, instead of explicitly setting them to zero. The introduced model assessment procedure monitors the out-of-sample predictive performance of the fitted model, and together with a list of guidelines we provide, one can investigate whether the hypothesised model is supported by the data. We incorporate scoring rules and cross-validation to supplement existing model assessment metrics for BSEM. The proposed tools can be applied to models for both continuous and binary data. The modelling of categorical and non-normally distributed continuous data is facilitated with the introduction of an item-individual random effect. We study the performance of the proposed methodology via simulation experiments as well as real data on the ‘Big-5’ personality scale and the Fagerstrom test for nicotine dependence.     

Stopping Rules for Computer Adaptive Testing When Item Banks Have Nonuniform Information

The standard error (SE) stopping rule, which terminates a computer adaptive test (CAT) when the SE is less than a threshold, is effective when there are informative questions for all trait levels. However, in domains such as patient-reported outcomes, the items in a bank might all target one end of the trait continuum (e.g., negative symptoms), and the bank may lack depth for many individuals. In such cases, the predicted standard error reduction (PSER) stopping rule will stop the CAT even if the SE threshold has not been reached and can avoid administering excessive questions that provide little additional information. By tuning the parameters of the PSER algorithm, a practitioner can specify a desired tradeoff between accuracy and efficiency. Using simulated data for the Patient-Reported Outcomes Measurement Information System Anxiety and Physical Function banks, we demonstrate that these parameters can substantially impact CAT performance. When the parameters were optimally tuned, the PSER stopping rule was found to outperform the SE stopping rule overall, particularly for individuals not targeted by the bank, and presented roughly the same number of items across the trait continuum. Therefore, the PSER stopping rule provides an effective method for balancing the precision and efficiency of a CAT.