Institute of Psychology, Chinese Academy of Sciences
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Development and Validation of Paradigms Based on the Global-First Topological Approach for Alzheimer's Disease Severity Staging
No full textIntroduction: Conventional methods like patient history, neuropsychological testing, cerebrospinal fluid examination, and magnetic resonance imaging are widely used to diagnose cases in the current clinical setting but are limited in classifying Alzheimer's disease (AD) stages. Patients with AD exhibit visual perception deficits, which may be a potential target to assess the severity of the disease according to visual paradigms. However, owing to the inconsistent forms of perceived objects, the defects of current visual processing paradigms often lead to inconsistent results and a lack of sensitivity and specificity. Methods: We develop two paradigms based on global -first topological approach of visual perception, which avoids inconsistent results and lack of sensitivity and specificity owing to the inconsistent forms of perceived objects in traditional paradigms, delineate a unique detection strategy from perception organization (Experiment 1) and visual working memory (VWM) (Experiment 2). Results: Except for the significant differences of the reaction times (RTs) between groups, significant differences were found when AD subjects recognize small figures due to the consistency of global and local figures in similarity test. The difference of RTs between recognizing global and local figures can be recognized in AD and mild cognitive impairment (MCI) group compared to healthy elderly (HE) in similarity test (Experiment 1). The memory capacity of AD patients was significantly lower than MCI group. Topological interference effect was observed in MCI and HE group, whereas MCI patients may have a greater difference trend in nontopological and topological changes than HE (Experiment 2). Conclusion: Our paradigms provide a new strategy, which can assist clinical severity staging and linking topological approach of visual perception with pathophysiological processes in AD.</p
Altered amplitude of low frequency-fluctuation and functional connectivity in first episode and recurrent major depressive disorder: a resting-state fMRI study
No full text目的:采用功能磁共振成像(fMRI)技术，基于低频振幅(ALFF)及静息态脑功能连接(rsFC),对比分析首发、复发抑郁症(MDD)患者和健康对照者之间脑自发活动及脑功能连接的差异，旨在探索首发及复发MDD的神经病理机制的差异。方法:前瞻性纳入2018年6月-2019年12月在本院就诊的47例首发MDD患者和35例复发MDD患者作为研究组。同期通过广告招募113例健康志愿者作为正常对照组。采集所有受试者的静息态fMRI数据，采用单因素协方差分析比较三组间各脑区ALFF值的差异，进而将有统计学差异的脑区作为种子点进行rsFC分析。结果:三组之间左侧颞上回和颞中回ALFF值的差异有统计学意义(P_(FWE)=0.007)。事后分析发现复发MDD组左侧颞上回和颞中回的ALFF值均高于首发MDD组和正常对照组，差异均有统计学意义(Ps<0.001,Bonferroni校正);而首发MDD组与对照组之间此指标值的差异无统计学意义(P>0.05,Bonferroni校正)。三组之间左侧颞上/中回-右侧楔前叶rsFC值的差异具有统计学意义(P_(FWE)=0.002)。事后分析发现首发和复发MDD组的rsFC值均高于对照组且差异有统计学意义(Ps<0.05,Bonferroni校正);复发组rsFC值高于首发组，但差异无统计学意义(P>0.05,Bonferroni校正)。结论:首发及复发MDD患者表现出左侧颞叶脑自发活动和左侧颞叶-楔前叶脑功能连接的异常及不同，这有助于区别首发及复发MDD的神经病理机制差异。</p
M3GIA: A Cognition Inspired Multilingual and Multimodal General Intelligence Ability Benchmark
No full textAs recent multi-modality large language models (MLLMs) have shown formidable proficiency on various complex tasks, there has been increasing attention on debating whether these models could eventually mirror human intelligence. However, existing benchmarks mainly focus on evaluating solely on task performance, such as the accuracy of identifying the attribute of an object. Combining well-developed cognitive science to understand the intelligence of MLLMs beyond superficial achievements remains largely unexplored. To this end, we introduce the first cognitive-driven multi-lingual and multi-modal benchmark to evaluate the general intelligence ability of MLLMs, dubbed M3GIA. Specifically, we identify five key cognitive factors based on the well-recognized Cattell-Horn-Carrol (CHC) model of intelligence and propose a novel evaluation metric. In addition, since most MLLMs are trained to perform in different languages, a natural question arises: is language a key factor influencing the cognitive ability of MLLMs? As such, we go beyond English to encompass other languages based on their popularity, including Chinese, French, Spanish, Portuguese and Korean, to construct our M3GIA. We make sure all the data relevant to the cultural backgrounds are collected from their native context to avoid English-centric bias. We collected a significant corpus of data from human participants, revealing that the most advanced MLLM reaches the lower boundary of human intelligence in English. Yet, there remains a pronounced disparity in the other five languages assessed. We also reveals an interesting winner takes all phenomenon that are aligned with the discovery in cognitive studies. Our benchmark will be open-sourced, with the aspiration of facilitating the enhancement of cognitive capabilities in MLLMs.</p
Exploring the possibilities of integration of cyber-psychology for human behaviour in a smart city
No full textThe smart city idea differs between cities and nations. In all meanings and characteristics of a smart city, public involvement is the only thing that remains common. Therefore, it is a very significant field to study human behaviour and development in smart cities. This paper presents a framework for identifying qualities necessary for people to be classified as intelligent persons and to integrate these human behavioural characteristics in cyber technology. Human behaviour in a smart city has been analysed using the machine learning algorithm and big data analytics. The integrated machine learning and big data analytics framework (iML-BD) classifies the cyber behaviour of intelligent persons in a smart city by observing the cyber activities performed by the individuals. Furthermore, this paper handles the risk factors for cyber-acquired and cyber-dependant crime violence and abuse that vulnerable internet and public access devices using blockchain technology. Blockchain is a method of storing data that takes too long to alter, modify, or manipulate. A blockchain is an electronic accounting system that is reproduced and spread through the Bitcoin protocol’s entire communication network. The case study performed on iML-BD has resulted in the highest performance in terms of prediction accuracy of 94.98%.</p
A comparison of statistical learning of naturalistic textures between DCNNs and the human visual hierarchy
No full textThe visual system continuously adapts to the statistical properties of the environment. Existing evidence shows a close resemblance between deep convolutional neural networks (CNNs) and primate visual stream in neural selectivity to naturalistic textures above the primary visual processing stage. This study delves into the mechanisms of perceptual learning in CNNs, focusing on how they assimilate the high-order statistics of natural textures. Our results show that a CNN model achieves a similar performance improvement as humans, as manifested in the learning pattern across different types of high-order image statistics. While L2 was the first stage exhibiting texture selectivity, we found that stages beyond L2 were critically involved in learning. The significant contribution of L4 to learning was manifested both in the modulations of texture-selective responses and in the consequences of training with frozen connection weights. Our findings highlight learning-dependent plasticity in the mid-to-high-level areas of the visual hierarchy. This research introduces an AI-inspired approach for studying learning-induced cortical plasticity, utilizing DCNNs as an experimental framework to formulate testable predictions for empirical brain studies.</p
Unconscious and Conscious Gaze-Triggered Attentional Orienting: Distinguishing Innate and Acquired Components of Social Attention in Children and Adults with Autistic Traits and Autism Spectrum Disord
No full textTypically developing (TD) individuals can readily orient attention according to others' eye-gaze direction, an ability known as social attention, which involves both innate and acquired components. To distinguish between these two components, we used a critical flicker fusion technique to render gaze cues invisible to participants, thereby largely reducing influences from consciously acquired strategies. Results revealed that both visible and invisible gaze cues could trigger attentional orienting in TD adults (aged 20 to 30 years) and children (aged 6 to 12 years). Intriguingly, only the ability to involuntarily respond to invisible gaze cues was negatively correlated with autistic traits among all TD participants. This ability was substantially impaired in adults with autism spectrum disorder (ASD) and in children with high autistic traits. No such association or reduction was observed with visible gaze cues. These findings provide compelling evidence for the functional demarcation of conscious and unconscious gaze-triggered attentional orienting that emerges early in life and develops into adulthood, shedding new light on the differentiation of the innate and acquired aspects of social attention. Moreover, they contribute to a comprehensive understanding of social endophenotypes of ASD.</p
Pragmatic language impairment in children with attention}leficit/hyperactivity disorder
No full text注意缺陷多动障碍(ADHD)是一种常见的神经发育障碍。大量研究显示，ADHD儿童常伴有语用性语言障碍。结合现有的文献资料，本文梳理了ADHD儿童的语用性语言障碍的临床表现，总结了ADHD儿童语用性语言障碍可能的发生机制，分析了以往研究中存在的问题，并为未来研究提出了方向。</p
Influence of parental body talk on adolescent health
No full text父母关于身体的谈论将影响青少年的体像发展。体像健康与否不仅影响身体健康，也对心理健康有深远影响。伴随着身体的快速发育和第二性征的逐渐成熟，青春期的个体格外关注自己的外貌，因而青春期也是体像发展的关键期。国内研究较少关注父母的言论对青少年体像健康的影响，父母也很少对相关的言论进行思考和反思。该文回顾了父母的各类言论（如批评或赞美等一般性评论、体重取笑、对进食的控制或鼓励节食，以及自贬性肥胖谈话等）对青少年体像影响的相关研究成果，探讨通过干预父母的言论来改善青少年体像的可能性。</p
A multidimensional analysis of self-esteem and individualism: A deep learning-based model for predicting elementary school students' academic performance
No full textThe current research descends into the discrete connection between self-esteem, individuality and academic achievement among primary school learners. It addresses the essential need for predictive algorithms to analyze and forecast academic results. Self-esteem and identity, as diverse psychological variables, have been highlighted as crucial determinants in creating academic success. The fundamental goal is to develop an effective prediction model for elementary school children's academic performance by including self-esteem and individuality in the analytical framework. We suggested an Owl Search Optimized Dynamic Deep Neural Network (OSO-DDNN) based model for making more precise predictions. The sample size for the research was 147,210 elementary school students who took the LegiLexi exam between 2016 and 2021. To standardize the data and improve model performance, the suggested methodology uses a pre-processing strategy based on the Min-Max scalar approach. Furthermore, Independent Component Analysis is used to detect and extract essential factors related to self-esteem, individuality and academic achievement. The architecture and parameters of the suggested approaches are optimized using the OSO algorithm to provide a prediction model that remains accurate and efficient. We quantify the effectiveness of the assessments using the following metrics: precision, recall, accuracy and F1-score to develop a design using conventional methods. The findings demonstrate that the model's resilience and promise for anticipating primary school pupils' academic success are based on psychological aspects. The OSO-DDNN is an essential instrument for educators and policymakers in identifying at-risk pupils and implementing targeted interventions, supporting a comprehensive approach to education that combines psychological well-being with academic performance.</p
Relationship between parental warmth and adolescent leadership:The mediating role of basic psychological needs satisfaction
No full text目的:基于领导力内隐认知理论和自我决定理论分别探讨父母情感温暖对青少年领导力的作用及其内在机制。方法:采用父母情感温暖问卷、基本心理需求满足量表和中学生领导力问卷等对云南地区某中学546名高中生展开研究。结果:(1)父亲(r=0.36,P<0.01)、母亲(r=0.34,P<0.01)情感温暖与青少年领导力均显著正相关；(2)中介效应检验表明，父亲、母亲情感温暖对青少年领导力的直接效应均显著，效应值分别为0.24和0.23,胜任需求满足在父亲、母亲情感温暖和青少年领导力之间起中介作用，中介效应值分别为0.08和0.07。结论:父母情感温暖不仅和青少年领导力密切相关，还可以通过满足青少年的胜任需求，从而促进青少年领导力的发展。</p
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