Computational models of the “active self” and its disturbances in schizophrenia

The notion that self-disorders are at the root of the emergence of schizophrenia rather than a symptom of the disease, is getting more traction in the cognitive sciences. This is in line with philosophical approaches that consider an enactive self, constituted through action and interaction with the environment. We thereby analyze different definitions of the self and evaluate various computational theories lending to these ideas. Bayesian and predictive processing are promising approaches for computational modeling of the “active self”. We evaluate their implementation and challenges in computational psychiatry and cognitive developmental robotics. We describe how and why embodied robotic systems provide a valuable tool in psychiatry to assess, validate, and simulate mechanisms of self-disorders. Specifically, mechanisms involving sensorimotor learning, prediction, and self-other distinction, can be assessed with artificial agents. This link can provide essential insights to the formation of the self and new avenues in the treatment of psychiatric disorders.     

发展性阅读障碍的听觉时间加工缺陷

发展性阅读障碍是指个体在智力正常并且不缺乏学校教育的情况下, 仍无法获得与年龄相匹配的阅读技能的一种学习障碍, 其缺陷的本质一直是研究者争论的焦点。大量研究显示, 阅读障碍者常表现出听觉时间加工损伤。在行为层面, 阅读障碍者难以辨别快速、连续呈现刺激的顺序以及刺激本身的动态时间特征。在神经层面, 阅读障碍者诱发的失匹配负波更弱且具有异常的神经同步加工。这些损伤同时存在于对言语和非言语刺激的加工中, 表明听觉时间加工缺陷非言语加工所特有。未来的研究还需阐明以下几个问题：1)阅读障碍的听觉时间加工缺陷发生在哪些时间窗口, 随年龄增长如何变化; 2)阅读障碍听觉时间加工缺陷在神经层面的时间进程是怎样的; 3)听觉时间加工缺陷是否为阅读障碍的核心缺陷。     

非符号数量表征和符号分数表征的关系

个体学习符号分数的一个关键是能对其数值形成准确表征。现有研究假设符号分数表征的认知基础是人类自婴幼儿期就具有的非符号数量表征(如表征两个集合各自的数量, 或两个数量的比例)。其证据包括表征非符号数量(尤其是非符号数量比例关系)和表征符号分数在行为和大脑神经活动层面上都表现出相关性。然而要说明非符号数量表征是符号分数表征的认知基础, 还需更多研究表明两者在数量概念上的独特相关和因果联系, 并阐明符号分数表征形成的认知机制。     

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。     

情绪加工中杏仁核的效价特异性

杏仁核是情绪信息加工的关键脑区。近年来心理学和神经科学领域发现了杏仁核情绪加工的效价特异性现象,并且整体存在左侧杏仁核对正性情绪、右侧杏仁核对负性情绪以及双侧杏仁核负性偏好的特异性趋势,且受到材料突出特征、个体差异、任务条件的调节。未来可进一步探索注意对杏仁核情绪效价特异性的调节作用,探究动态情绪刺激加工时杏仁核的活动特点,考察心理障碍患者加工负性情绪时的杏仁核激活模式,并确定杏仁核的效价特异性在思维、计划、决策等高级认知过程中的表现。     

多级评分认知诊断模型述评

认知诊断评估旨在探讨个体内部的知识掌握结构,并提供关于学生优缺点的详细诊断信息,以促进个体的全面发展。当前研究者已开发了大量0-1评分的认知诊断模型,但对于多级评分认知诊断模型的研究还比较少。本文对已有的多级评分认知诊断模型进行了归纳,介绍了模型的假设,计量特征以及适用范围,为实际应用者和研究者在多级评分认知诊断模型的比较和选用上提供借鉴和参考。最后,对未来关于多级评分诊断模型的研究方向进行了展望。     

Understanding the role of configural processing in face emotion recognition in Parkinson’s disease

This investigation examined whether impairment in configural processing could explain deficits in face emotion recognition in people with Parkinson’s disease (PD). Stimuli from the Radboud Faces Database were used to compare recognition of four negative emotion expressions by older adults with PD (n = 16) and matched controls (n = 17). Participants were tasked with categorizing emotional expressions from upright and inverted whole faces and facial composites; it is difficult to derive configural information from these two types of stimuli so featural processing should play a larger than usual role in accurate recognition of emotional expressions. We found that the PD group were impaired relative to controls in recognizing anger, disgust and fearful expressions in upright faces. Then, consistent with a configural processing deficit, participants with PD showed no composite effect when attempting to identify facial expressions of anger, disgust and fear. A face inversion effect, however, was observed in the performance of all participants in both the whole faces and facial composites tasks. These findings can be explained in terms of a configural processing deficit if it is assumed that the disruption caused by facial composites was specific to configural processing, whereas inversion reduced performance by making it difficult to derive both featural and configural information from faces.     

The effects of statistical training on choice heuristics in choice under uncertainty

Kahneman and Tversky (1984) proposed that decision makers perceive choice uncertainty in two ways: (1) as a distribution of possible outcomes or (2) as a single uncertain outcome. Using statistical training as a factor that influences these perceptions, and thus the type of decision approach individuals use, we found that individuals with different levels of experience displayed differences in the decisions they made and in the choice heuristics used to make those decisions. Statistically naive individuals were more likely to prefer loss-minimizing alternatives, use a more non-compensatory heuristic, and spend more time on loss-related information than their statistically experienced counterparts. When a distributional cue, indicating the distributional nature of choice outcomes, was presented to both experience groups, the naive group was found to use a decision approach similar to the experienced group and to make similar decisions. The results are discussed in terms of the need to include factors that alter individuals' approaches to uncertainty in future behavioral models of uncertain choice.     

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.     

Testing two variances for superiority/non-inferiority and equivalence: Using the exhaustion algorithm for sample size allocation with cost

The equality of two group variances is frequently tested in experiments. However, criticisms of null hypothesis statistical testing on means have recently arisen and there is interest in other types of statistical tests of hypotheses, such as superiority/non-inferiority and equivalence. Although these tests have become more common in psychology and social sciences, the corresponding sample size estimation for these tests is rarely discussed, especially when the sampling unit costs are unequal or group sizes are unequal for two groups. Thus, for finding optimal sample size, the present study derived an initial allocation by approximating the percentiles of an F distribution with the percentiles of the standard normal distribution and used the exhaustion algorithm to select the best combination of group sizes, thereby ensuring the resulting power reaches the designated level and is maximal with a minimal total cost. In this manner, optimization of sample size planning is achieved. The proposed sample size determination has a wide range of applications and is efficient in terms of Type I errors and statistical power in simulations. Finally, an illustrative example from a report by the Health Survey for England, 1995–1997, is presented using hypertension data. For ease of application, four R Shiny apps are provided and benchmarks for setting equivalence margins are suggested.