Do Humans and Deep Convolutional Neural Networks Use Visual Information Similarly for the Categorization of Natural Scenes?

The investigation of visual categorization has recently been aided by the introduction of deep convolutional neural networks (CNNs), which achieve unprecedented accuracy in picture classification after extensive training. Even if the architecture of CNNs is inspired by the organization of the visual brain, the similarity between CNN and human visual processing remains unclear. Here, we investigated this issue by engaging humans and CNNs in a two-class visual categorization task. To this end, pictures containing animals or vehicles were modified to contain only low/high spatial frequency (HSF) information, or were scrambled in the phase of the spatial frequency spectrum. For all types of degradation, accuracy increased as degradation was reduced for both humans and CNNs; however, the thresholds for accurate categorization varied between humans and CNNs. More remarkable differences were observed for HSF information compared to the other two types of degradation, both in terms of overall accuracy and image-level agreement between humans and CNNs. The difficulty with which the CNNs were shown to categorize high-passed natural scenes was reduced by picture whitening, a procedure which is inspired by how visual systems process natural images. The results are discussed concerning the adaptation to regularities in the visual environment (scene statistics); if the visual characteristics of the environment are not learned by CNNs, their visual categorization may depend only on a subset of the visual information on which humans rely, for example, on low spatial frequency information.     

不同频率经颅交流电刺激在精神疾病中的应用

近年来, 研究者开始将经颅交流电刺激技术应用于精神疾病领域中, 其中, 以γ、α频率最受关注。tACS作用于精神疾病的可能机制包括两个方面, 直接调节异常的大脑神经活动和间接改善患者的认知功能。首先, 使用特定频率的tACS作用目标脑区, 可以调节对应频率的神经振荡和大脑功能连接, 通过作用于疾病相关的异常大脑活动, 直接改善患者的临床症状。其次, 并不针对某种疾病特异受损的大脑活动, 而是用tACS激活与认知功能相关的大脑环路, 广泛地提高患者的注意、记忆等多种认知功能, 进而整体上缓解不良症状。目前, 使用tACS治疗精神疾病这一领域还有一些尚未解决、值得探讨的问题。tACS的作用机制研究、刺激参数和范式改进、技术升级, 可以成为心理学、脑科学以及临床医学的重点研究方向。     

创造性认知重评在情绪调节中的迁移效应及其神经基础

认知重评在负性情绪调节中发挥着重要的作用。针对传统认知重评重构程度不够高、调节效果不明显的问题, 研究团队在先前的工作中提出了一种使消极情绪“转负为正”的高效情绪调节方法, 即创造性认知重评, 这种调节策略伴随着大脑海马的新颖联结形成和杏仁核的积极情绪唤醒。然而这些工作采用“指导式”的重评更像是对重评解读的“理解”而非主动的情绪调节; 鉴于主动情绪调节的产生难度较大, 造成创造性认知重评在实际应用上的两难困境。基于此, 本项目拟指导受试者进行创造性认知重评的学习, 通过学习将这种策略迁移并且应用到生活中的负性情绪调节事件中。具体研究内容包括：(1)行为学上, 探索创造性认知重评在大学生群体和青少年群体负性情绪调节中的迁移效应; (2)影像学上, 探索创造性认知重评迁移前后在脑认知表征模式上的变化。本项目是对已有工作基础的延伸和拓展, 为验证和推动创造性认知重评成为一种可学、可用、高效的情绪调节策略奠定理论基础。     

序列依赖效应——一种全新的“历史效应”

自2014年来, 研究者在视觉加工中发现一种全新的历史效应——序列依赖效应(当前刺激加工向先前刺激方向偏移的吸引性加工偏差)。近期研究发现：该效应广泛存在于视觉加工的各个层面(既包括朝向、空间位置、数量等低级特征, 也包括身份、吸引力、美感等高级属性); 其来源极为复杂(包括感觉编码、高级皮层的反馈调节、工作记忆、决策模板、感知与决策的级联等), 反映出不同层次的过往加工痕迹向当前认知的投射。针对该效应的典型特征、影响因素、认知与神经机制, 已涌现大量研究, 同时也存在严重争论, 亟待研究者深入探讨和厘清。     

A constructive neural-network approach to modeling psychological development

This article reviews a particular computational modeling approach to the study of psychological development – that of constructive neural networks. This approach is applied to a variety of developmental domains and issues, including Piagetian tasks, shift learning, language acquisition, number comparison, habituation of visual attention, concept learning, and theory of mind. Implications of this modeling for theoretical understanding of psychological development are considered.     

跨期选择的认知机制与神经基础

跨期选择是指个体对发生在不同时间的成本与收益进行权衡的决策过程。跨期选择的计算模型从经济学的角度用数学模型来建构时间折扣函数,而认知成分模型则从心理学的角度来研究跨期选择中的心理效应与认知成分。跨期选择的神经基础有三种不同的研究取向：双机制加工取向、单机制加工取向、自我控制取向。未来研究应该在跨期选择的认知机制、神经通路及运行机制、跨期选择的应用,以及从进化的角度对人与动物的跨期选择行为进行更深入的研究。     

Dissociable neural systems of sequence learning

Although current theories all point to distinct neural systems for sequence learning, no consensus has been reached on which factors crucially define this distinction. Dissociable judgment-linked versus motor-linked and implicit versus explicit neural systems have been proposed. This paper reviews these two distinctions, yet concludes that these traditional dichotomies prove insufficient to account for all data on sequence learning and its neural organization. Instead, a broader theoretical framework is necessary providing a more continuous means of dissociating sequence learning systems. We argue that a more recent theory, dissociating multidimensional versus unidimensional neural systems, might provide such framework, and we discuss this theory in relation to more general principles of associative learning and recent imaging findings.     

Autonomy in Action: Linking the Act of Looking to Memory Formation in Infancy via Dynamic Neural Fields

Looking is a fundamental exploratory behavior by which infants acquire knowledge about the world. In theories of infant habituation, however, looking as an exploratory behavior has been deemphasized relative to the reliable nature with which looking indexes active cognitive processing. We present a new theory that connects looking to the dynamics of memory formation and formally implement this theory in a Dynamic Neural Field model that learns autonomously as it actively looks and looks away from a stimulus. We situate this model in a habituation task and illustrate the mechanisms by which looking, encoding, working memory formation, and long‐term memory formation give rise to habituation across multiple stimulus and task contexts. We also illustrate how the act of looking and the temporal dynamics of learning affect each other. Finally, we test a new hypothesis about the sources of developmental differences in looking.     

A Modeling Study of Potential Sources of Curvature in Human Reaching Movements

The authors of this article suggest that the slight but consistent posture-dependent curvature of the spatial paths in the kinematic transformation between intrinsic and extrinsic coordinates may result in a systematic curvature of movements initially planned as straight-line trajectories toward the target. A kinematic planning model is presented that takes into account the anisotropy of the intrinsic and extrinsic transformation and tends to avoid movements that require excessive joint rotations by introducing slight deviations from a straight-line trajectory. Preliminary simulations showed reasonably good agreement with experimental data, especially considering that the current model is strictly based on kinematics. A quantitative analysis showed that the strategy used in the model achieves a favorable compromise between straight-line movements and angular joint changes: By slightly increasing the spatial length of the movement (i.e., by introducing curvature), an individual can greatly reduce the total amount of joint rotation required to produce the movement.     

The Utility of Connectionism for Motor Learning: A Reinterpretation of Contextual Interference in Movement Schemas

Using the general framework of schema theory, and building on it, the present article takes a connectionist approach to motor learning and to contextual interference effects. These phenomena were simulated in an exploratory manner in neural networks. The outcome closely reflects previous research with humans. In a simulated ballistic movement task, networks performed worse during practice but showed better transfer when target movement distances were presented in a random rather than a blocked fashion. Connectionism provides a parsimonious account of the effect in terms of properties inherent in the parallel distributed network.