Declarative working memory: A bio-inspired cognitive architecture proposal

Memory is considered one of the most important functions since it allows us to code, store and retrieve knowledge. These qualities make it an indispensable function for a virtual creature. In general, memory can be classified based on the durability of the stored data in working memory and long-term memory. Working memory refers to the capacity to maintain temporarily a limited amount of information in mind, which can then be used to support various abilities, including learning, reasoning, planning and decision-making. Unlike short-term memory, working memory is not only a storage site, but it is also a framework of interacting processes that involve the temporary storage and manipulation of information in the service of performing complex cognitive activities. Declarative memory is a type of long-term memory related with the storage of facts and events. This research focuses on the development of a cognitive architecture for the type of working memory that maintains and manipulates declarative information. The construction of the model was grounded in theoretical evidence taken from cognitive sciences such as neuroscience and psychology, which gave us the components and their processes. The model was evaluated through a case study that covers the encoding, storing, and retrieval stages. Our hypothesis is that a virtual creature endowed with our working memory model will provide faster access to the information needed for the ongoing task. Therefore, it improves the planning and decision-making processes.     

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.     

神经美学视角的审美愉悦加工机制

审美对象特有的刺激属性会唤起观赏者特定的情绪或情感反应。个体在欣赏自然、艺术品和其他人类作品时会产生审美愉悦体验。审美愉悦-兴趣模型(PIA)认为, 审美愉悦体验包含审美过程中自动化加工阶段的审美愉悦和控制加工阶段的审美兴趣。近年来, 神经美学研究表明, 负责愉悦和奖赏的眶额叶皮层在审美过程中广泛激活, 是自动化加工阶段初级审美愉悦奖赏的神经基础, 而审美过程中纹状体亚回路中不同的连接和功能作用与两个阶段中审美愉悦的产生都有关联; 上述结果支持了审美愉悦-兴趣模型。但审美高峰体验时默认模式网络(DMN)相关脑区的激活和负责控制与理性思维的外侧前额叶皮层等脑区的失活, 提示在PIA模型强调的自动化加工阶段审美愉悦和控制加工阶段审美兴趣之上, 还有整合升华阶段的审美沉浸愉悦, PIA模型需得到进一步的扩展。未来研究应进一步检验审美愉悦认知加工模型及神经机制, 探索审美对创造力的影响机制和神经基础, 探讨不同审美经验愉悦机制的异同。     

不患不均？容忍和支持经济不平等的心理学解释

不断加剧的经济不平等问题对个体和社会有着巨大危害,然而人们对经济不平等却有着较高的容忍性。基于个体心理的研究证据,本文提出认知和动机双重路径模型来解释个体容忍和支持经济不平等问题。在认知路径上,个体倾向于低估当前社会的经济不平等程度和将经济不平等评价为公平的;在动机路径上,个体预期经济不平等将带来自我利益的增加。未来研究应进一步整合多重心理机制间的关系,并探索有效干预手段以增加人们对减少经济不平等的支持。     

混合情绪能促进心理健康吗？

摘 要 积极和消极情绪的同时体验,即混合情绪,是生活中常见的情绪状态。健康应对的共激活模型、情绪动态模型和积极比模型一致认为混合情绪与心理健康之间呈现非线性的关系,适当的混合情绪有利于个体在困境中全面加工和整合信息,寻求意义,获得控制感,进而实现健康应对和心理症状改善,获得幸福感。未来研究应关注对非线性关系的精确描述;低压力或非压力情境下混合情绪的意义;进一步研究混合情绪促进心理健康的内部机制和影响因素。     

多时点结果跨期选择的决策效应及其心理机制

多时点结果跨期选择涉及两个及两个以上多个时点结果的权衡, 正日益成为跨期选择的研究重点。现有研究主要从两时点结果和序列结果两方面深入探讨了多时点结果跨期选择中的诸多决策效应, 包括：隐藏的零效应、象征效应、额外金额效应、前端金额效应以及序列效应, 并运用序列模型和权衡模型对上述决策效应进行阐释。未来还应深入探索同时涉及损益结果混合的多时点结果跨期选择, 运用决策过程追踪技术揭示决策效应的过程机制。     

阴谋论信念的产生机制——社会认知视角的三种解释

社会心理学将阴谋论视作一种意识形态上的信念,并定义为人们将重大的政治或社会事件归因为有权力的群体或个人暗中预谋以达成其目的的解释倾向。社会认知视角为个体这种阴谋论信念的产生提供了三种解释。错觉模式感知导致人们认知上倾向于在不相关的事件之间建立联系;敏感性动因觉察影响人们过度感知并假定事件背后的动因、目的和意图等;投射是将自己愿意参与阴谋的意图归因于事件中的他人。     

心理因素与学业表现：机器学习分类预测模型

随着高等教育规模的扩大,学业表现不良逐渐成为一个不容忽视的现象,对学业表现不良的学生进行预测并提早给予干预可降低退学率并减少教育资源的损失。由于导致学业表现不良的因素众多且关系复杂,传统的基于相关分析的研究方法很难建立早期预测模型并进行应用。本研究旨在利用机器学习算法,对数据进行挖掘,并建立学业表现预测模型。研究对653名大一新生的心理健康状况、应对方式、人格、内外控倾向和相关人口统计学信息进行了收集,并在一年后采集了其学业成绩,利用随机森林(RF)、K邻近（KNN）、支持向量机（SVM）、决策树（DT）、朴素贝叶斯（NB）等机器学习算法建立了分类模型。结果显示,随机森林算法在识别学业表现不良学生时有最好的表现,其中准确率95.86%, 召回率91.83%,f1分数为93.80%。特征重要性分析显示,前10个对模型有最高贡献度的特征包括：年龄、性别、是否为独生子、内外控倾向、神经质倾向、积极应对倾向、宜人性倾向、一般症状指数、开放性倾向和焦虑水平。为避免过度拟合问题,本研究在一年后收集的166名新生样本中进行了模型验证,结果显示模型在新数据集上有较好的泛化表现,其中f1分数90.90％,准确率92.60％,召回率89.26％。研究提示基于人口统计学和心理测评信息,机器学习算法有助于及早识别学业表现不良学生并为开展早期干预提供启示。     

Depression screening and treatment among uninsured populations in Primary Care

Uninsured populations have poor treatment engagement and are less likely to receive evidence-based interventions for depression. The objective of the current study was to retrospectively examine depression screening, diagnosis, and treatment patterns among uninsured patients in primary care. Study sample included all patients (N = 11,803) seen in nine community-based clinics. Key variables included depression screener and/or a depression diagnosis, anti-depressant initiation, behavioral health visits, and patient follow up measures. Treatment patterns from the subsample of patients diagnosed with depression were analyzed by collecting the number of behavioral health visits and antidepressant use six months (180 days) following the diagnosis. Utilization of the depression screening tool was high (67%, n = 7,935) and 24% (n = 2,789) of the patients had a diagnosis of depression, however, more than half of the patients with a depression diagnosis did not have a recorded treatment plan (n = 1,474). The odds of anti-depressant use and behavioral visits for Hispanic patients were significantly greater than for Non-Hispanic patients. Universal screening with brief measures in primary care is improving, however, guideline-concordant depression treatment remains elusive for uninsured populations.