Analysing the influence of attitude and habit on bicycle commuting

New transport trends have emerged in developing countries to promote bicycling due to its individual and societal benefits. Although bicycle infrastructure provision has been one of the most utilised strategies to encourage bicycling, it has had a limited immediate impact on the bicycling modal share. Published research indicates the need to incorporate the psychosocial dimension to understand commuters' transport behaviour and explain why bicycling infrastructure usage is lower than expected. This study highlights the processes behind bicycle mode choice decisions, explicitly incorporating pro-bicycle attitudes and habits, while also considering the influence of socioeconomic, bicycle facilities and bicycling experience variables on shaping that attitude. For this purpose, an online survey was designed and sent to students, faculty members and staff of two Chilean universities to collect ad-hoc data. The modelling used an approach based on an integrated choice and latent variable model. The main findings are: attitude in itself is a relevant construct to explain behaviour; bicycling infrastructure contribute to explain bicycle choice but only indirectly through attitude; socioeconomic characteristics, bicycling familiarity and practical issues have both a direct effect on bicycle choice decision and an indirect one by fostering pro-bicycling attitudes. Additionally, results show that the explicit inclusion of habit reduces the contribution of pro-bicycle attitude in explaining bicycle choice. This might be due to interaction and reinforcement between habit and attitude.     

An adaptive network model covering metacognition to control adaptation for multiple mental models

Learning processes can be described by adaptive mental (or neural) network models. If metacognition is used to regulate learning, the adaptation of the mental network becomes itself adaptive as well: second-order adaptation. In this paper, a second-order adaptive mental network model is introduced for metacognitive regulation of learning processes. The focus is on the role of multiple internal mental models, in particular, the case of visualisation to support learning of numerical or symbolic skills. The second-order adaptive network model is illustrated by a case scenario for the role of visualisation to support learning multiplication at the primary school.     

Decision-making bioinspired model for target definition and “satisfactor” selection for physiological needs

Every person, from an early age, has to make decisions to resolve situations that arise in life. In general, different people make different decisions in the same situation, since decision-making takes into account different factors such as age, emotional state, experience, among others. We can make decisions about situations that we classify as: more important than others, routine, unexpected, or trivial. However, making the correct decision(s) in a timely manner for these situations is one of the most complex and delicate challenges that human beings face. This is due to the arduous mental process required to be carried out. Providing such behavior to a virtual entity is possible through the use of Cognitive Architectures (CAs). CAs are an approach for modeling human intelligence and behavior. This paper presents an functional bioinspired computational decision-making model to satisfy the physiological needs of hunger and thirst. Our proposal considers as black boxes other cognitive functions that are part of a general CA (named Cuäyöllötl or brain in Nahuatl). In the proposed case study, it is proved that the decision-making process plays an essential role in determining the objective and selecting the object that satisfies the established need.     

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.     

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。     

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。