The role of midfrontal theta oscillations across the development of cognitive control in preschoolers and school‐age children

The development of cognitive control enables children to better resist acting based on distracting information that interferes with the current action. Cognitive control improvement serves different functions that differ in part by the type of interference to resolve. Indeed, resisting to interference at the task‐set level or at the response‐preparation level is, respectively, associated with cognitive flexibility and inhibition. It is, however, unknown whether the same neural mechanism underlies these two functions across development. Studies in adults have revealed the contribution of midfrontal theta (MFT) oscillations in interference resolution. This study investigated whether MFT is involved in the resolution of different types of interference in two age groups identified as corresponding to different latent structures of executive functions. Preschool (4–6 years) and school children (6–8 years) were tested with a task involving interference at the response level and/or the task‐set level while (electroencephalogram) EEG was recorded. Behaviorally, response time and accuracy were affected by task‐set. Both age groups were less accurate when the interference occurred at the task‐set level and only the younger group showed decreased accuracy when interference was presented at the response‐preparation level. Furthermore, MFT power was increased, relative to the baseline, during the resolution of both types of interference and in both age groups. These findings suggest that MFT is involved in immature cognitive control (i.e., preschool and school‐ages), by orchestrating its different cognitive processes, irrespective of the interference to resolve and of the level of cognitive control development (i.e., the degree of differentiation of executive functions).     

Mapping differential responses to cognitive training using machine learning

We used two simple unsupervised machine learning techniques to identify differential trajectories of change in children who undergo intensive working memory (WM) training. We used self‐organizing maps (SOMs)—a type of simple artificial neural network—to represent multivariate cognitive training data, and then tested whether the way tasks are represented changed as a result of training. The patterns of change we observed in the SOM weight matrices implied that the processes drawn upon to perform WM tasks changed following training. This was then combined with K‐means clustering to identify distinct groups of children who respond to the training in different ways. Firstly, the K‐means clustering was applied to an independent large sample (N = 616, M_age = 9.16 years, range = 5.16–17.91 years) to identify subgroups. We then allocated children who had been through cognitive training (N = 179, M_age = 9.00 years, range = 7.08–11.50 years) to these same four subgroups, both before and after their training. In doing so, we were able to map their improvement trajectories. Scores on a separate measure of fluid intelligence were predictive of a child's improvement trajectory. This paper provides an alternative approach to analysing cognitive training data that go beyond considering changes in individual tasks. This proof‐of‐principle demonstrates a potentially powerful way of distinguishing task‐specific from domain‐general changes following training and of establishing different profiles of response to training.     

Toward ethical cognitive architectures for the development of artificial moral agents

New technologies based on artificial agents promise to change the next generation of autonomous systems and therefore our interaction with them. Systems based on artificial agents such as self-driving cars and social robots are examples of this technology that is seeking to improve the quality of people’s life. Cognitive architectures aim to create some of the most challenging artificial agents commonly known as bio-inspired cognitive agents. This type of artificial agent seeks to embody human-like intelligence in order to operate and solve problems in the real world as humans do. Moreover, some cognitive architectures such as Soar, LIDA, ACT-R, and iCub try to be fundamental architectures for the Artificial General Intelligence model of human cognition. Therefore, researchers in the machine ethics field face ethical questions related to what mechanisms an artificial agent must have for making moral decisions in order to ensure that their actions are always ethically right. This paper aims to identify some challenges that researchers need to solve in order to create ethical cognitive architectures. These cognitive architectures are characterized by the capacity to endow artificial agents with appropriate mechanisms to exhibit explicit ethical behavior. Additionally, we offer some reasons to develop ethical cognitive architectures. We hope that this study can be useful to guide future research on ethical cognitive architectures.     

工作反刍及其“双刃剑”效应

工作反刍指在没有要求其出现的情境下, 有意识的、重复发生的工作方面的相关想法, 分为工作相关情感反刍与工作相关问题解决沉思两个维度。工作反刍对个体健康、工作及幸福感的影响有“双刃剑”效应。持续性认知理论、压力认知激活模型与认知资源视角理论, 可以解释该效应的内在机制。未来研究应分析影响双刃剑效应的边界条件, 寻求减少消极效应、增加积极效应的途径, 拓展其双刃剑效应心理机制的分析视角和该效应的作用层面。     

价值观的稳定性与可变性：基于认知神经科学的视角

价值观的稳定性和可变性一直是该领域争论的焦点所在。近些年来, 研究者开始从认知神经科学的角度来探讨导致价值观稳定性差异的机制问题。已有的神经反应证据表明, 当某种价值观是基于道义主义的绝对规则来进行认知建构的, 或与自我概念在表征上存在一致性时, 其更倾向于保持稳定的状态; 若非如此, 便可能会在外界影响(如他人劝说)下发生重要程度上的改变。未来需要进一步丰富和完善认识价值观稳定性与可变性关系的认知神经加工模型, 并探索价值观长效改变背后的认知神经机制, 以促进理论研究在价值观教育中的实践和应用。     

Resistance and outcome expectations in cognitive‐behavioural therapy for generalised anxiety disorder

Greater client resistance has been consistently found to be negatively associated with outcome in psychotherapy. However, the pathways through which resistance impacts such outcomes are underexplored. Given that client outcome expectation (OE) has been identified as an important common factor in psychotherapy, the goals of the present study were to examine: (a) the impact of resistance on subsequent client and therapist OE (COE & TOE, respectively); and (b) whether COE and/or TOE mediate the relationship between resistance and outcome. These relationships were tested among 44 clients with severe generalised anxiety disorder treated with cognitive‐behavioural therapy in the context of a randomised controlled trial (Westra, Constantino, & Antony, 2016). Resistance was measured at a mid‐treatment session, and COE and TOE were assessed at baseline and immediately after the resistance session. Treatment outcome was measured via client‐rated worry severity at post‐treatment. As predicted, higher resistance was associated with lower subsequent COE and TOE; B = ?.73, p < .001 and B = ?.46, p < .001, respectively. In turn, lower post‐resistance COE predicted higher post‐treatment worry (B = ?.5, p < .001), indicating mediation. In contrast, TOE did not mediate the relationship between resistance and outcome (B = ?.02, p = .876). These results suggest that resistance can be demoralising to both clients and therapists. However, only lower client morale may be detrimental to therapy outcome. This study contributes to understanding outcome pathways through two common therapy processes.     

Network change point detection in resting-state functional connectivity dynamics of mild cognitive impairment patients

Background/Objective: This study aims to characterize the differences on the short-term temporal network dynamics of the undirected and weighted whole-brain functional connectivity between healthy aging individuals and people with mild cognitive impairment (MCI). The Network Change Point Detection algorithm was applied to identify the significant change points in the resting-state fMRI register, and we analyzed the fluctuations in the topological properties of the sub-networks between significant change points. Method: Ten MCI patients matched by gender and age in 1:1 ratio to healthy controls screened during patient recruitment. A neuropsychological evaluation was done to both groups as well as functional magnetic images were obtained with a Philips 3.0T. All the images were preprocessed and statistically analyzed through dynamic point estimation tools. Results: No statistically significant differences were found between groups in the number of significant change points in the functional connectivity networks. However, an interaction effect of age and state was detected on the intra-participant variability of the network strength. Conclusions: The progression of states was associated to higher variability in the patient's group. Additionally, higher performance in the prospective and retrospective memory scale was associated with higher median network strength.