The phenomenon of task-irrelevant perceptual learning

Task-irrelevant perceptual learning (TIPL) has captured a growing interest in the field of perceptual learning. The basic phenomenon is that stimulus features that are irrelevant to a subject’s task (i.e. convey no useful information to that task) can be learned due to their consistent presentation during task-performance. Here we review recent research on TIPL and focus on two key aspects of TIPL; (1) the mechanisms gating learning in TIPL, and (2) what is learned through TIPL. We show that TIPL is gated by learning signals that are triggered from task processing or by rewards. These learning signals operate to enhance processing of individual stimulus features and appear to result in plasticity in early stages of visual processing. Furthermore, we discuss recent research that demonstrates that TIPL is not in opposition to theories of attention but instead that TIPL operates in concert with attention. Where attentional learning is best to enhance (or suppress) processing of stimuli of known task relevance, TIPL serves to enhance perception of stimuli that are originally inadequately processed by the brain.     

Evaluating the progress of deep learning for visual relational concepts

Convolutional neural networks have become the state-of-the-art method for image classification in the last 10 years. Despite the fact that they achieve superhuman classification accuracy on many popular datasets, they often perform much worse on more abstract image classification tasks. We will show that these difficult tasks are linked to relational concepts from cognitive psychology and that despite progress over the last few years, such relational reasoning tasks still remain difficult for current neural network architectures. We will review deep learning research that is linked to relational concept learning, even if it was not originally presented from this angle. Reviewing the current literature, we will argue that some form of attention will be an important component of future systems to solve relational tasks. In addition, we will point out the shortcomings of currently used datasets, and we will recommend steps to make future datasets more relevant for testing systems on relational reasoning.     

Assessing the functions underlying learning using by-trial and by-participant models: Evidence from two visual perceptual learning paradigms

Inferred mechanisms of learning, such as those involved in improvements resulting from perceptual training, are reliant on (and reflect) the functional forms that models of learning take. However, previous investigations of the functional forms of perceptual learning have been limited in ways that are incompatible with the known mechanisms of learning. For instance, previous work has overwhelmingly aggregated learning data across learning participants, learning trials, or both. Here we approach the study of the functional form of perceptual learning on the by-person and by-trial levels at which the mechanisms of learning are expected to act. Each participant completed one of two visual perceptual learning tasks over the course of two days, with the first 75% of task performance using a single reference stimulus (i.e., “training”) and the last 25% using an orthogonal reference stimulus (to test generalization). Five learning functions, coming from either the exponential or the power family, were fit to each participant''s data. The exponential family was uniformly supported by Bayesian Information Criteria (BIC) model comparisons. The simplest exponential function was the best fit to learning on a texture oddball detection task, while a Weibull (augmented exponential) function tended to be the best fit to learning on a dot-motion discrimination task. The support for the exponential family corroborated previous by-person investigations of the functional form of learning, while the novel evidence supporting the Weibull learning model has implications for both the analysis and the mechanistic bases of the learning.     

PBL结合CBL教学法在眼科护理学教学中的应用

目的:探讨PBL(problem-based learning)教学法结合CBL(case-based learning)教学法在眼科护理学教学中的应用及教学效果,为眼科护理学教学改革提供依据。方法:随机抽取2007级高职护理专业2个班(105例)并随机分为试验组(55例)和对照组(50例),试验组采用PBL教学法结合CBL教学法完成眼科护理学教学任务,对照组采取传统式教学法完成教学任务。授课结束后,对两个班期末理论考试成绩、学生对所授课程满意度及授课教师评优率进行比较。结果:试验组与对照组比较以上3项均具有统计学意义(P<0.01)。结论:PBL教学法结合案例教学法在眼科护理学教学中教学效果优于传统教学法,教学效果良好,有助于培养学生的综合素质。     

人工智能在圆锥角膜中的应用研究进展

圆锥角膜是一种高发于青少年的致盲性角膜疾病,早期诊疗可有效减少疾病晚期造成的视力损害并改善其预后。基于机器学习和深度学习的人工智能(AI)在圆锥角膜领域的研究主要包括圆锥角膜的早期筛查诊断和严重度分级、圆锥角膜进展预测及术后疗效预测等。文章总结近年常见的AI在圆锥角膜中的主要应用研究进展,并对其面临的挑战与未来前景进行展望。     

PBL教学模式在循证医学教学中的实践与应用

目的：探索PBL教学模式在本科生循证医学教学中的应用效果。
　　方法：从2010级临床医学专业本科生中随机选择5个班,共147人作为试验组,采用“以问题为基础的教学(problem-based learning,PBL)”模式,另外随机选择5个班,共149人采用“以授课为基础的教学( lecture-based learning,LBL)”模式作为对照。对两组学生的期末考试成绩进行比较,同时采用调查表对学生进行问卷调查,获得他们对PBL的评价信息。采用SPSS 13.0软件对所有数据进行统计学处理。
　　结果：基线调查的均衡性检验的结果显示,两组学生间的基本特征无显著性差异,具有可比性(P >0.05)。期末考试的结果显示,两组学生除了对EBM的基础知识考核的结果无显著性差异外(P >0.05),对EBM的过程的5个步骤,即提出问题、寻找最佳证据、评价证据、应用和实践证据、并对证据进行再评估,以及总成绩的评价显示,两组间均存在显著性差异,并具有统计学意义(P<0.05)。学生对学习效果评价的结果显示,两组学生对采用PBL方式在更好消化课堂所学内容、提高语言表达能力,以及在锻炼写作能力的应答无显著性差异(P>0.05),而其他各项均具有显著性差异(P<0.05)。尤其在调动学习积极性、提高自学能力、提高学习效率、提高信息分析与利用能力、加强团队协作意识,以及加强师生交流沟通方面,两组间的应答具有极其显著性差异(P<0.001)。
　　结论：PBL 教学模式能有效提高 EBM 的教学效果,改善EBM的教学质量,值得进一步推广应用。     

人工智能在干眼诊断中的研究进展

干眼(dry eye, DE)是世界范围内最常见的眼科疾病之一,患病率在5%～50%。由于病因复杂且诊断的相应设备有限,干眼尚不能得到及时、精准的诊断。近年来,随着人工智能(artificial intelligence,AI)在医学领域的广泛应用,利用机器学习和深度学习辅助检查干眼也得到了深入研究,如干涉测量、裂隙灯检查和睑板腺图像的分类和评估等。研究发现人工智能能够对干眼患者的测量数据和图像进行准确分析,灵敏度和特异度均可达90%以上。人工智能将在辅助临床医生客观诊断干眼、改善干眼患者生活质量方面具有巨大潜力。在这篇综述中,我们总结了人工智能在干眼领域的应用现状以及应用中潜在的挑战,展望了人工智能辅助诊断干眼的前景。     

When attention interrupts learning: Inhibitory effects of attention on TIPL

While task-irrelevant perceptual learning (TIPL) suggests that perceptual learning of a feature occurs without focused attention to the feature, some kind of attentional involvement was implied by recent findings that TIPL occurred only when a task-irrelevant stimulus was paired with a main task target. Here, during training, two task-irrelevant stimuli with different coherent motion directions were exposed, one on an attended side and the other on an unattended side. We found no performance improvements for the direction on the attended side. These results suggest that while attention facilitates task-relevant learning, it can suppress TIPL.     

人工智能在眼前段疾病诊治中的应用

近年来,人工智能(AI)的蓬勃发展促进了其在医疗保健领域的推广与应用,同时也促进了医疗保健技术的革新与进步,尤其是在图像识别领域发挥出了无可替代的作用。眼科疾病的诊断十分依赖图像识别,AI在眼前段疾病的诊治方面取得了令人瞩目的成果,如感染性角膜炎的分类、圆锥角膜的筛查、晶状体混浊程度的分级、白内障手术视频的自动分期、白内障术后屈光状态的预测、闭角型青光眼的诊断等。AI有望帮助解决目前临床存在的诸多难题,实现对疾病的早期诊治,但也存在着黑箱过程难以解释、缺少公共数据集、算法过于复杂等问题。本文概述了AI在眼前段疾病中的应用现状,分析目前存在的不足以及未来的发展前景。     

Artificial intelligence in glaucoma

Background:Artificial Intelligence (AI) is an area of computer science that encompasses the creation of intelligent machines that work and react like humans. It deals with the development algorithms that seek to simulate human brain and also mimic cognitive functions typically associated with the human mind such as learning and problem solving.Purpose:Do we need artificial intelligence in Glaucoma? Glaucoma is the second most common cause of blindness in the world. Its prevalence was over 60 million in 2010 and over 80 million by 2020. It is so common, yet so easily overlooked. More importantly, about 50% of patients in developed countries and 90% in developing countries are unaware of having glaucoma. Early detection can delay the progression of glaucoma. Hence the time is ripe to advovate glaucoma screening.Synopsis:The application of AI in ophthalmology mainly concentrates on the diseases with a high incidence, such as diabetic retinopathy, age-related macular degeneration, glaucoma, retinopathy of prematurity, age-related or congenital cataract etc AI involves mainly 1. machine learning that are algorithms with the ability to learn without being explicitly programmed and 2. deep learning in which artificial neural networks adapt and learn from vast amounts of data. But there are limitations to screening - such as disparity between ophthalmologist:patient ratio and also the availability of the specialty services. The large amount of data acquired from patients makes it nearly impossible for ophthalmologists to screen them with equal efficacy and consistency.Highlights:AI in glaucoma aims at including factors such as clinical data, genomic data, life style behaviors, risk factors, and medical history to predict the risk of developing glaucoma, help customise the most appropriate management protocol for a given patient, and estimate prognosis and surgical success.Video Link: https://youtu.be/IwYS7wDMhkY     

Visual learning by cue-dependent and cue-invariant mechanisms

We examined learning at multiple levels of the visual system. Subjects were trained and tested on a same/different slant judgment task or a same/different curvature judgment task using simulated planar surfaces or curved surfaces defined by either stereo or monocular (texture and motion) cues. Taken as a whole, the results of four experiments are consistent with the hypothesis that learning takes place at both cue-dependent and cue-invariant levels, and that learning at these levels can have different generalization properties. If so, then cue-invariant mechanisms may mediate the transfer of learning from familiar cue conditions to novel cue conditions, thereby allowing perceptual learning to be robust and efficient. We claim that learning takes place at multiple levels of the visual system, and that a comprehensive understanding of visual perception requires a good understanding of learning at each of these levels.     

Artificial intelligence assisted pterygium diagnosis: current status and perspectives

Pterygium is a prevalent ocular disease that can cause discomfort and vision impairment. Early and accurate diagnosis is essential for effective management. Recently, artificial intelligence (AI) has shown promising potential in assisting clinicians with pterygium diagnosis. This paper provides an overview of AI-assisted pterygium diagnosis, including the AI techniques used such as machine learning, deep learning, and computer vision. Furthermore, recent studies that have evaluated the diagnostic performance of AI-based systems for pterygium detection, classification and segmentation were summarized. The advantages and limitations of AI-assisted pterygium diagnosis and discuss potential future developments in this field were also analyzed. The review aims to provide insights into the current state-of-the-art of AI and its potential applications in pterygium diagnosis, which may facilitate the development of more efficient and accurate diagnostic tools for this common ocular disease.     

Application of artificial intelligence in ophthalmology

Artificial intelligence is a general term that means to accomplish a task mainly by a computer, with the least human beings participation, and it is widely accepted as the invention of robots. With the development of this new technology, artificial intelligence has been one of the most influential information technology revolutions. We searched these English-language studies relative to ophthalmology published on PubMed and Springer databases. The application of artificial intelligence in ophthalmology mainly concentrates on the diseases with a high incidence, such as diabetic retinopathy, age-related macular degeneration, glaucoma, retinopathy of prematurity, age-related or congenital cataract and few with retinal vein occlusion. According to the above studies, we conclude that the sensitivity of detection and accuracy for proliferative diabetic retinopathy ranged from 75% to 91.7%, for non-proliferative diabetic retinopathy ranged from 75% to 94.7%, for age-related macular degeneration it ranged from 75% to 100%, for retinopathy of prematurity ranged over 95%, for retinal vein occlusion just one study reported ranged over 97%, for glaucoma ranged 63.7% to 93.1%, and for cataract it achieved a more than 70% similarity against clinical grading.     

Current state and future prospects of artificial intelligence in ophthalmology: a review

Artificial intelligence (AI) has emerged as a major frontier in computer science research. Although AI has broad application across many medical fields, it will have particular utility in ophthalmology and will dramatically change the diagnostic and treatment pathways for many eye conditions such as corneal ectasias, glaucoma, age‐related macular degeneration and diabetic retinopathy. However, given that AI has primarily been driven as a computer science, its concepts and terminology are unfamiliar to many medical professionals. Important key terms such as machine learning and deep learning are often misunderstood and incorrectly used interchangeably. This article presents an overview of AI and new developments relevant to ophthalmology.     

人工智能在眼整形外科中的应用

计算机的进步和数据的爆发使得人类迎来了第三次人工智能(AI)浪潮。AI是一门综合性的交叉学科，是汇集新思想、新理论、新技术等的新兴学科。AI给眼科学带来了便利，也推动了眼科学的智能化、精准化和微创化发展。现阶段，AI已经在眼科学多个领域中都得到了广泛的应用，尤其在眼整形外科领域中，AI在图像检测、面部识别等方面取得了快速进展，其性能及准确度在某些方面甚至已经超越了人类。本文综述了AI在上睑下垂、单睑、眼袋、眼睑肿物及眼球突出等眼整形外科中的相关研究和应用，探讨了当前AI在眼整形外科临床应用中面临的挑战与机遇，并对其未来发展前景进行展望，旨在为眼整形外科AI的发展提供新思路。     

Bibliometric analysis of artificial intelligence and optical coherence tomography images: research hotspots and frontiers

AIM: To explore the latest application of artificial intelligence (AI) in optical coherence tomography (OCT) images, and to analyze the current research status of AI in OCT, and discuss the future research trend. METHODS: On June 1, 2023, a bibliometric analysis of the Web of Science Core Collection was performed in order to explore the utilization of AI in OCT imagery. Key parameters such as papers, countries/regions, citations, databases, organizations, keywords, journal names, and research hotspots were extracted and then visualized employing the VOSviewer and CiteSpace V bibliometric platforms. RESULTS: Fifty-five nations reported studies on AI biotechnology and its application in analyzing OCT images. The United States was the country with the largest number of published papers. Furthermore, 197 institutions worldwide provided published articles, where University of London had more publications than the rest. The reference clusters from the study could be divided into four categories: thickness and eyes, diabetic retinopathy (DR), images and segmentation, and OCT classification. CONCLUSION: The latest hot topics and future directions in this field are identified, and the dynamic evolution of AI-based OCT imaging are outlined. AI-based OCT imaging holds great potential for revolutionizing clinical care.     

Boosting perceptual learning by fake feedback

How does the brain control its sensory plasticity using performance feedback? We examined this question using various types of fake feedback in perceptual learning paradigm. We demonstrated that fake feedback indicating a larger performance improvement facilitated learning compared with genuine feedback. Variance of the fake feedback modulated learning as well, suggesting that feedback uncertainty can be internally evaluated. These results were explained by a computational model which controlled the learning rate of the visual system based on Bayesian estimation of performance gradient incorporating an optimistic bias. Our findings suggest that sensory plasticity might be controlled by high-level cognitive processes.     

Learning to see again: Perceptual learning of simulated abnormal on- off-cell population responses in sighted individuals

Many forms of artificial sight recovery, such as electronic implants and optogenetic proteins, generally cause simultaneous, rather than complementary firing of on- and off-center retinal cells. Here, using virtual patients—sighted individuals viewing distorted input—we examine whether plasticity might compensate for abnormal neuronal population responses. Five participants were dichoptically presented with a combination of original and contrast-reversed images. Each image (I) and its contrast-reverse (Iʹ) was filtered using a radial checkerboard (F) in Fourier space and its inverse (Fʹ). [I * F′] + [Iʹ * F] was presented to one eye, and [I * F] + [Iʹ * F′] was presented to the other, such that regions of the image that produced on-center responses in one eye produced off-center responses in the other eye, and vice versa. Participants continuously improved in a naturalistic object discrimination task over 20 one-hour sessions. Pre-training and post-training tests suggest that performance improvements were due to two learning processes: learning to recognize objects with reduced visual information and learning to suppress contrast-reversed image information in a non–eye-selective manner. These results suggest that, with training, it may be possible to adapt to the unnatural on- and off-cell population responses produced by electronic and optogenetic sight recovery technologies.     

将基于问题式学习融入眼科学教学初探

将"基于问题式学习"(problem-based learning,PBL)融入眼科学实习教学中的教学改革,受到老师和学生的欢迎。PBL教学模式充分发挥了学生的主观能动性,在培养学生临床技能、临床思维及自学能力方面体现出了明显优势,同时,带教老师的综合素质也得到了提高。实践证明,PBL教学模式是培养创新能力型人才的有效方法之一。