创造力产生过程中的神经振荡机制

创造力究竟是怎么产生的, 目前尚未得出一致的结论。神经电生理技术因其高时间分辨率, 可以准确地揭示创造力产生进程中的神经振荡机制, 从而帮助人们更深刻地理解创造力的本质。近年来的研究发现, 单节律alpha神经振荡会随着创造力的增加而增强, 这反映了创造力产生过程中的内部信息加工需求增加、自上而下的抑制控制增强。同时, 多频段神经振荡交叉节律耦合体现了创造性产生过程中额叶、颞叶和顶叶等多脑区之间信息交流的动态变化。未来研究应该以整合理论框架为基础, 结合多层次多方法的研究工具, 引进更生态化的数理计算方法, 并利用计算神经科学建模来预测个体创造力发展趋势, 从而全面深刻地认识创造力的本质。     

中国人限制性饮食和食物渴求的认知神经机制

物质丰富的时代, 如何抵御美味食物线索带来的食物渴求, 并限制饮食从而保持健康成为人们关注的重要话题之一。近年来, 国内学者为了探讨节食成功的原因, 创建中国食物图片库, 考察限制性饮食的认知神经机制, 以及成功和失败限制性饮食者的脑机制差异; 开展了特质食物渴求及中国特色的辣食渴求的认知神经研究。对推进饮食行为研究的理论发展, 也为肥胖、饮食失调等健康干预工作提供重要支持。     

Research in counselling and psychotherapy Post‐COVID‐19

The COVID‐19 pandemic brings to light many areas the field of counselling and psychotherapy may need to address in future research. We outline several issues stemming from or exacerbated by the pandemic and offer suggestions for future research to address the mental health needs of those impacted. Our suggestions focus on five domains: (a) the health and well‐being of helping professionals, (b) the infodemic, (c) discrimination and minority stress, (d) spiritual and existential dynamics in mental health and (e) couple and family stress and resilience. We aim to provide a multi‐systemic perspective of mental health and well‐being in the time of COVID‐19, as well as encourage current and future studies to incorporate these suggestions to advance the health and well‐being of our communities through evidence‐based treatment approaches.     

Gerontological Counseling Trends in Adultspan Journal: 1999–2019

Using quantitative content analysis, we categorized 76 out of 154 articles (49.35%) published in Adultspan Journal from 1999 to 2019 as focused on gerontological counseling. This study explored publication trends for these articles and compared results with a larger study (Fullen et al., 2019) on publication trends for gerontological counseling across all counseling‐related journals.     

A systematic review of the effectiveness of machine learning for predicting psychosocial outcomes in acquired brain injury: Which algorithms are used and why?

Clinicians working in the field of acquired brain injury (ABI, an injury to the brain sustained after birth) are challenged to develop suitable care pathways for an individual client’s needs. Being able to predict psychosocial outcomes after ABI would enable clinicians and service providers to make advance decisions and better tailor care plans. Machine learning (ML, a predictive method from the field of artificial intelligence) is increasingly used for predicting ABI outcomes. This review aimed to examine the efficacy of using ML to make psychosocial predictions in ABI, evaluate the methodological quality of studies, and understand researchers’ rationale for their choice of ML algorithms. Nine studies were reviewed from five databases, predicting a range of psychosocial outcomes from stroke, traumatic brain injury, and concussion. Eleven types of ML were employed with a total of 75 ML models. Every model was evaluated as having high risk of bias, unable to provide adequate evidence for predictive performance due to poor methodological quality. Overall, there was limited rationale for the choice of ML algorithms and poor evaluation of the methodological limitations by study authors. Considerations for overcoming methodological shortcomings are discussed, along with suggestions for assessing the suitability of data and suitability of ML algorithms for different ABI research questions.     

A Theoretical Framework for Analysing the Notion of Relative Importance of Criteria

Multiple-criteria decision aid almost always requires the use of weights, importance coefficients or even a hierarchy of criteria, veto thresholds, etc. These are importance parameters that are used to differentiate the role devoted to each criterion in the construction of comprehensive preferences. Many researchers have studied the problem of how to assign values to such parameters, but few of them have tried to analyse in detail what underlies the notion of importance of criteria and to give a clear formal definition of it. In this paper our purpose is to define a theoretical framework so as to analyse the notion of the importance of criteria under very general conditions. Within this framework it clearly appears that the importance of criteria is taken into account in very different ways in various aggregation procedures. This framework also allows us to shed new light on fundamental questions such as: Under what conditions is it possible to state that one criterion is more important than another? Are importance parameters of the various aggregation procedures dependent on or independent of the encoding of criteria? What are the links between the two concepts of the importance of criteria and the compensatoriness of preferences? This theoretical framework seems to us sufficiently general to ground further research in order to define theoretically valid elicitation methods for importance parameters.     

The Nature of Client Agency Prior to Therapy: A Qualitative Study on Clients’ Narratives

Client agency is considered a crucial contributor to good treatment outcome. Recent studies, however, differ strongly in how they conceptualise and investigate agency. The current study explores the nature of client agency in ten clients’ pre-treatment interviews. Applying Consensual Qualitative Research, we constructed three overarching categories, subdivided into 14 sub-categories capturing both between- and within-person differences in agency before therapy. We found that all participants oscillated between the experience of a lack of grip on problems on the one hand and noticing their involvement in the problem and taking action on the other. These results present a dynamic conceptualisation of client agency. This allows us to ask pertinent questions for both future research and clinical practice.     

Generating interior search directions for multiobjective linear programming

A new multiobjective linear programming (MOLP) algorithm is presented. The algorithm uses a variant of Karmarkar's interior-point algorithm known as the affine-scaling primal algorithm. Using this single-objective algorithm, interior search directions are generated and used to provide an approximation to the gradient of the (implicitly known) utility function. The approximation is guided by assessing locally relevant preference information for the various interior directions through interaction with a decision maker (DM). The resulting algorithm is an interactive approach that makes its progress towards the solution through the interior of the constraints polytope.     

Selecting a new technology strategy using multiattribute,multilevel decision trees

Research indicates that selecting a strategy to best exploit a new technology is a complex decision-making process. The task involves making a series of decisions with multiple alternatives, each to be evaluated by multiple criteria whose values have high levels of uncertainty. This paper presents a methodology for modelling a new technology decision using decision trees and an optimizing algorithm. A problem of a mining company considering the adoption of new technology is used to illustrate the decision-making task and modelling methodology. A numerical solution to the case demonstrates the potential of the optimizing technique in strategy selection.