The Paradox of Human Expertise: Why Experts Can Get It Wrong

Expertise is correctly, but one-sidedly, associated with special abilities and enhanced performance. The other side of expertise, however, is surreptitiously hidden. Along with expertise, performance may also be degraded, culminating in a lack of flexibility and error. Expertise is demystified by explaining the brain functions and cognitive architecture involved in being an expert. These information processing mechanisms, the very making of expertise, entail computational trade-offs that sometimes result in paradoxical functional degradation. For example, being an expert entails using schemas, selective attention, chunking information, automaticity, and more reliance on top-down information, all of which allow experts to perform quickly and efficiently; however, these very mechanisms restrict flexibility and control, may cause the experts to miss and ignore important information, introduce tunnel vision and bias, and can cause other effects that degrade performance. Such phenomena are apparent in a wide range of expert domains, from medical professionals and forensic examiners, to military fighter pilots and financial traders

A review of research into the development of radiologic expertise: Implications for computer-based training

Rationale and Objectives. Studies of radiologic error reveal high levels of variation between radiologists. Although it is known that experts outperform novices, we have only limited knowledge about radiologic expertise and how it is acquired.Materials and Methods. This review identifies three areas of research: studies of the impact of experience and related factors on the accuracy of decision-making; studies of the organization of expert knowledge; and studies of radiologists' perceptual processes.Results and Conclusion. Interpreting evidence from these three paradigms in the light of recent research into perceptual learning and studies of the visual pathway has a number of conclusions for the training of radiologists, particularly for the design of computer-based learning programs that are able to illustrate the similarities and differences between diagnoses, to give access to large numbers of cases and to help identify weaknesses in the way trainees build up a global representation from fixated regions

A participatory design approach for the development of support environments in eGovernment services to citizens

The introduction of eGovernment services and applications leads to major changes in the structure and operation of public administrations. In this paper we describe the work in progress in an Italian project called “SPO.T.” aimed at the analysis, development, deployment and evaluation of tools and environments to support the people who plan, deliver, use and evaluate user-centred provision of One-Stop-Shop services to citizens. The “SPO.T.” project has focused on two requirements: 1. the support tools and environments must facilitate the active involvement of all stakeholders in the definition and evolution of eGovernment applications and services, and it is argued that through participatory design changes of structure, process and culture can be delivered effectively; 2. they must embody a set of architecturally coherent resources which reflect the new roles and relationships of public administration and which are sufficiently generic to be relevant to a wide range of local contexts across the community

Spoken word recognition in adolescents with autism spectrum disorders and specific language impairment

Spoken word recognition, during gating, appears intact in specific language impairment (SLI). This study used gating to investigate the process in adolescents with autism spectrum disorders plus language impairment (ALI). Adolescents with ALI, SLI, and typical language development (TLD), matched on nonverbal IQ listened to gated words that varied in frequency (low/high) and number of phonological onset neighbors (low/high density). Adolescents with ALI required more speech input to initially identify low-frequency words with low competitor density than those with SLI and those with TLD, who did not differ. These differences may be due to less well specified word form representations in ALI

Synthetic Observational Health Data with GANs: from slow adoption to a boom in medical research and ultimately digital twins?

After being collected for patient care, Observational Health Data (OHD) can further benefit patient well-being by sustaining the development of health informatics and medical research. Vast potential is unexploited because of the fiercely private nature of patient-related data and regulations to protect it. Generative Adversarial Networks (GANs) have recently emerged as a groundbreaking way to learn generative models that produce realistic synthetic data. They have revolutionized practices in multiple domains such as self-driving cars, fraud detection, digital twin simulations in industrial sectors, and medical imaging. The digital twin concept could readily apply to modelling and quantifying disease progression. In addition, GANs posses many capabilities relevant to common problems in healthcare: lack of data, class imbalance, rare diseases, and preserving privacy. Unlocking open access to privacy-preserving OHD could be transformative for scientific research. In the midst of COVID-19, the healthcare system is facing unprecedented challenges, many of which of are data related for the reasons stated above. Considering these facts, publications concerning GAN applied to OHD seemed to be severely lacking. To uncover the reasons for this slow adoption, we broadly reviewed the published literature on the subject. Our findings show that the properties of OHD were initially challenging for the existing GAN algorithms (unlike medical imaging, for which state-of-the-art model were directly transferable) and the evaluation synthetic data lacked clear metrics. We find more publications on the subject than expected, starting slowly in 2017, and since then at an increasing rate. The difficulties of OHD remain, and we discuss issues relating to evaluation, consistency, benchmarking, data modelling, and reproducibility.Comment: 31 pages (10 in previous version), not including references and glossary, 51 in total. Inclusion of a large number of recent publications and expansion of the discussion accordingl

Awake Reactivation of Prior Experiences Consolidates Memories and Biases Cognition.

After experiences are encoded into memory, post-encoding reactivation mechanisms have been proposed to mediate long-term memory stabilization and transformation. Spontaneous reactivation of hippocampal representations, together with hippocampal-cortical interactions, are leading candidate mechanisms for promoting systems-level memory strengthening and reorganization. While the replay of spatial representations has been extensively studied in rodents, here we review recent fMRI work that provides evidence for spontaneous reactivation of nonspatial, episodic event representations in the human hippocampus and cortex, as well as for experience-dependent alterations in systems-level hippocampal connectivity. We focus on reactivation during awake post-encoding periods, relationships between reactivation and subsequent behavior, how reactivation is modulated by factors that influence consolidation, and the implications of persistent reactivation for biasing ongoing perception and cognition