Idiographic network analysis of discrete mood states prior to treatment

Idiographic network models based on time‐series data have received recent attention for their ability to model relationships among symptoms and behaviours as they unfold in time within a single individual (cf. Epskamp, Borsboom, & Fried, 2018; Fisher, Medaglia, & Jeronimus, 2018). Rather than examine the correlational relationships between variables in a sample of individuals, an idiographic network examines correlations within a single person, averaged over many time points. Because the approach averages over time, the data must be stationary (i.e. relatively consistent over time). If individuals experience varying states over time—different mixtures of symptoms and behaviours in one moment or another—then averaging over categorically different moments may undermine model accuracy. Fisher and Bosley (2019) address these concerns via the application of Gaussian finite mixture modelling to identify latent classes of time points in intraindividual time‐series data from a sample of adults with major depressive disorder and/or generalised anxiety disorder (n = 45). The present paper outlines an extension of this work, wherein network analysis is used to model within‐class covariation of symptoms. To illustrate this approach, network models were constructed for each intraindividual class identified by Fisher and Bosley (137 networks across the 45 participants, mean classes/person = ~3, range = 2–4 classes/person). We examine the relative consistency in symptom organisation between each individual's multiple mood state networks and assess emergent group‐level patterns. We highlight opportunities for enhanced treatment personalisation and review nomothetic patterns relevant to transdiagnostic conceptualisations of psychopathology. We address opportunities for integrating this approach into clinical practice and outline potential shortcomings.     

变量间的网络分析模型及其应用

变量间的网络分析模型近年来被广泛应用于心理学研究。不同于将潜变量作为观测变量的共同先导因素的潜变量模型, 网络分析模型将观测变量作为初级指标, 采用图论的方法建立观测变量之间的关系网络, 其中变量为网络的节点, 而变量间的关系是节点之间的连线。因此网络分析可以突显观测变量之间的联系以及观测变量相互影响而形成的系统。通过变量网络中基于各个节点特征的指标(如中心性)以及基于整体结构特征的指标(如小世界性), 网络分析为研究各种心理现象提供了新的可视化的描述方式和理解视角。近10年来, 网络分析的方法已在人格心理学、社会心理学和临床心理学等领域得到一定的应用。未来研究应继续发展和完善网络分析模型的理论和方法, 使之运用到更多的数据类型和更广的研究领域中。     

青少年的同伴选择与同伴影响：基于学业投入与学业成就的纵向社会网络分析

以756名中学生为研究对象,对学业投入、学业成就、同伴团体进行追踪测量,探讨青少年同伴团体的学业投入及学业成就是否存在同伴选择与同伴影响效应。结果表明：（1）在1年的追踪时间中,青少年学业投入的发展呈下降趋势,初始水平与下降速度负相关,学业成就的发展呈上升趋势,初始水平与上升速度无显著相关;（2）学业投入与学业成就存在同伴选择效应,青少年倾向选择与自己学业投入及学业成就相似的个体构建同伴团体;（3）青少年的学业投入及学业成就与同伴逐渐趋近相似,即同伴影响效应显著。     

Behavior analysis and revaluation

Revaluation refers to phenomena in which the strength of an operant is altered by reinforcer-related manipulations that take place outside the conditioning situation in which the operant was selected. As an example, if lever pressing is acquired using food as a reinforcer and food is later paired with an aversive stimulus, the frequency of lever pressing decreases when subsequently tested. Associationist psychology infers from such findings that conditioning produces a response-outcome (i.e., reinforcer) association and that the operant decreased in strength because pairing the reinforcer with the aversive stimulus changed the value of the outcome. Here, we present an approach to the interpretation of these and related findings that employs neural network simulations grounded in the experimental analysis of behavior and neuroscience. In so doing, we address some general issues regarding the relations among behavior analysis, neuroscience, and associationism.     

Neural correlates of a default response in a delayed go/no-go task

Working memory, the ability to temporarily retain task-relevant information across a delay, is frequently investigated using delayed matching-to-sample (DMTS) or delayed Go/No-Go tasks (DGNG). In DMTS tasks, sample cues instruct the animal which type of response has to be executed at the end of a delay. Typically, performance decreases with increasing delay duration, indicating that working memory fades across a delay. However, no such performance decrease has been found when the sample cues exist of present vs. absent stimuli, suggesting that pigeons do not rely on working memory, but seem to respond by default in those trials. We trained 3 pigeons in a DGNG task and found a similar default response pattern: The diverging slopes of the retention functions on correct Go and No-Go trials suggested that pigeons by default omitted their response following No-Go stimuli, but actively retained task-relevant information across the delay for successful responses on Go trials. We conducted single-cell recordings in the avian nidopallium caudolaterale, a structure comparable to the mammalian prefrontal cortex. On Go trials, many neurons displayed sustained elevated activity during the delay preceding the response, replicating previous findings and suggesting that task-relevant information was neurally represented and maintained across the delay. However, the same units did not show enhanced delay activity preceding correct response suppressions in No-Go trials. This activation-inactivation pattern presumably constitutes a neural correlate of the default response strategy observed in the DGNG task.     

Getting the Big Picture in Community Science: Methods That Capture Context

Community science has a rich tradition of using theories and research designs that are consistent with its core value of contextualism. However, a survey of empirical articles published in the American Journal of Community Psychology shows that community scientists utilize a narrow range of statistical tools that are not well suited to assess contextual data. Multilevel modeling, geographic information systems (GIS), social network analysis, and cluster analysis are recommended as useful tools to address contextual questions in community science. An argument for increased methodological consilience is presented, where community scientists are encouraged to adopt statistical methodology that is capable of modeling a greater proportion of the data than is typical with traditional methods.     

时间记忆层次网络模型的实验检验

实验采用分类性实验材料,使被试在对词单项目加工时形成较清晰的群集,进而形成时间组块,目的在于对时间记忆层次网络进行直接检验。其中时序判断用反时间时和正确率两种反应指标,时距估计用再现和口头估计两种方法。结果发现,时间信息记忆既存在层次网络的特征,又存在线性结构的特征。     

On the nature of international sales and sales management research: a social network–analytic perspective

This research uses a combination of text mining, co-word analysis, and social network analysis (SNA) to review 132 international sales and sales management (ISSM) articles published between 1980 and 2017. The study provides a unique view of the past and future of ISSM research and provides three principal contributions. First, from a social network–analytic perspective, it offers a unique examination of the ways ISSM research topics are interconnected, as reflected by keyword network structure. Second, by conducting SNA across two periods (1980–1999 and 2000–2017) and examining the changes in network centrality measures, the study offers initial insights into the evolving nature of the ISSM literature. Third, the study reports keyword network disconnections (i.e., structural holes) to propose a fruitful agenda for future ISSM research. Taken together, this research offers a current perspective of the ISSM research domain's evolving nature.     

Differences in Beliefs About Psychological Services in the Relationship Between Sociorace and One's Social Network

The roles of previous psychological service use and social network variables in beliefs about psychological services were examined with 184 college students. Having friends and family members who used psychological services, being female, and having used psychological services positively related with beliefs about psychological services. Socioracial differences in the effects of previous use of psychological services and social network variables on students’ beliefs about psychological services were found. Suggestions for ways counselors can provide support to students are discussed.