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Introduction: Artificial intelligence (AI) is one of the most considered topics of the current time. AI has the power to bring revolutionary improvements to the world of technology not only in the field of computer science but also in other fields like medical sciences. Objectives: This paper assumes the adoption of appropriate AI engineering principals in previous studies, and focusses on providing a structured review of the impact of AI on human society and the individual human being as a technology user. Additionally, it opens a window on how the future will look like in terms of AI and personalised medicine. Methods: The paper employed a qualitative research approach and data were collected through a structured literature review. Twenty-three peer reviewed papers were identified and analysed in relation to their relevance to the study. Results: Previous studies show a positive impact on users' behaviour is expected in supporting their healthcare needs especially in decision-making, personalised treatment and future diseases prediction, and that integrating users in studying AI impact is essential to test possible implications of the technology. Conclusion: Results indicate that without a clear understanding of why patients need AI, or how AI can support individuals with their healthcare needs, it is difficult to visualise the kinds of AI applications that have a meaningful and sustainable impact the daily lives of individuals. Therefore, there is an emerging need to understand the impact of AI technology on users' behaviour to maximise the potential benefits of AI technology.
The synergy between neurotransmitter spillover and asynchronous release in information transmission
(2018)
Interaction with the visual world critically depends on when and how we move our eyes. Eye movements, in turn, alter our experience of the world by selecting objects to foveate and tracking objects over time. The interaction between vision and eye movements continues indefinitely, one process modulating the strength and timing of the other. Despite this tight linkage, vision is often studied outside the context of eye movements, during stable fixation. Likewise, eye movements are often studied with sparse visual stimuli and under ideal viewing conditions. A more accurate understanding of vision requires studying perception in the context of eye movements.
Different types of eye movements reveal different aspects of how the brain works. The continuous nature of tracking smooth pursuit eye movements, combined with its ubiquitous presence in natural vision, makes it an excellent system in which to analyze perception at fine spatial and temporal resolutions. For example, how does vision change during the transition from fixation to pursuit, and when does the brain allocate resources to account for these changes? In contrast, the ballistic nature of saccades requires analysis divided into discrete epochs, including well before the saccade during the planning stage, during saccade execution, and after the movement. At the neuronal level, how do heterogeneous populations of visual- and saccade-related cells come to represent a single saccade vector during these different epochs? What are the effects on visual perception when saccades and pursuit interact? This symposium will reveal how research incorporating both types of eye movements and visual perception can provide a more ecologically valid account of visual-motor integration and the intervening cognitive processes.
This symposium will review innovative approaches and recent advances in understanding the interplay of eye movements and vision. We will take a multifaceted approach to understanding the effects of both exploratory (saccades) and tracking (smooth pursuit) eye movements on vision by using a range of methodologies and animal models (data-driven modeling, clinical populations, and simultaneous recordings of neuronal populations; humans, macaques, and marmosets). This symposium will appeal to vision scientists interested in selective attention, motion processing, neuronal modeling, motor control, neural circuits, and related topics. Our collective goal is to uncover the dynamics of visual perception and the candidate neuronal mechanisms that support eye movements.
In 1868, Karl Vierordt discovered one type of errors in time perception—an overestimation of short duration and underestimation of long durations, known as Vierordt's law. Here we reviewed the original study in its historical context and asked whether Vierordt's law is a result of an unnatural experimental randomization protocol. Using iterative Bayesian updating, we simulated the original results with high accuracy. Importantly, the model also predicted that a slowly changing random-walk sequence produces less central tendency than a random sequence with the same durations. This was validated by a duration reproduction experiment from two sequences (random and random walk) with the same sampled distribution. The results showed that trial-wise variation influenced the magnitude of Vierordt's law. We concluded that Vierordt's law is caused by an unnatural yet widely used experimental protocol.