Cognitive science is the interdisciplinary scientific study of the mind and its processes. It examines what cognition is, what it does and how it works. It includes research on intelligence and behavior, especially focusing on how information is represented, processed, and transformed (in faculties such as perception, language, memory, reasoning, and emotion) within nervous systems (human or other animal) and machines (e.g. computers). Cognitive science consists of multiple research disciplines, including psychology, artificial intelligence, philosophy, neuroscience, linguistics, and anthropology. The fundamental concept of cognitive science is "that thinking can best be understood in terms of representational structures in the mind and computational procedures that operate on those structures." Wikipedia (en)
Part 1 in the Modeling the Mind review series. 52 pages, 5 figures
Modeling the Mind: a brief review is an annual review, available for free on arXiv.org, whose aim is to help students and researchers unfamiliar with the field of neuroscience and computational neuroscience gain insight into the fundamentals of this domain of study. Creating an accurate simulation of the mind is no easy task, and while it took brilliant minds decades to advance us to where we’re at right now, we are still ways off our final goal. It is therefore imperative to have more research carried out in this multidisciplinary field, taking in help from researchers in biology, neuroscience, computer science, but also mathematics, physics, chemistry and imaging, in order to speed up this process and tip the scales in our favor for the upcoming decades. This annual review hopes to provide the required information for anyone who is considering this domain as his future endeavor. The reviews will be tackling relatively global characteristics at first in order to familiarize the reader with the basic foundations, and will be getting progressively more specific and in tune with current research in the upcoming parts. This is Part I. It will contain basic information about the computational aspect of this field, and will attempt to explain why certain concepts are generally agreed upon, and the intuition behind them, going through the essential founding works.
The brain is a powerful tool used to achieve amazing feats. There have been several significant advances in neuroscience and artificial brain research in the past two decades. This article is a review of such advances, ranging from the concepts of connectionism, to neural network architectures and high-dimensional representations. There have also been advances in biologically inspired cognitive architectures of which we will cite a few. We will be positioning relatively specific models in a much broader perspective, while comparing and contrasting their advantages and weaknesses. The projects presented are targeted to model the brain at different levels, utilizing different methodologies.
"The work was created by teams from Dutch museums Mauritshuis and Rembranthuis, alongside Microsoft, ING and the Delft University of Technology. Creating a faithful replication of a Rembrandt painting required huge amounts of data, with the team describing it was a "marriage" between technology and art. (...) With the help of several art experts, 346 Rembrandt paintings – digitised using 3D scans – were analysed by a deep learning algorithm. The algorithm isolated common Rembrandt subjects to create the "most consistent subject" – a white, middle aged man with facial hair, "wearing black clothes with a white collar and a hat". "
LA JOLLA—Salk researchers and collaborators have achieved critical insight into the size of neural connections, putting the memory capacity of the brain far higher than common estimates. The new work also answers a longstanding question as to how the brain is so energy efficient and could help engineers build computers that are incredibly powerful but also conserve energy.
A widespread problem in biological research is assessing whether a model adequately describes some real-world data. But even if a model captures the large-scale statistical properties of the data, should we be satisfied with it? We developed a method, inspired by Alan Turing, to assess the effectiveness of model fitting. We first built a self-propelled particle model whose properties (order and cohesion) statistically matched those of real fish schools. We then asked members of the public to play an online game (a modified Turing test) in which they attempted to distinguish between the movements of real fish schools or those generated by the model. Even though the statistical properties of the real data and the model were consistent with each other, the public could still distinguish between the two, highlighting the need for model refinement. Our results demonstrate that we can use ‘citizen science’ to cross-validate and improve model fitting not only in the field of collective behaviour, but also across a broad range of biological systems.
The claim that the hormone oxytocin promotes trust in humans has drawn a lot of attention. But today, a group of researchers reported that they’ve been unable to reproduce their own findings concerning that effect.
In short, money, wealth and power seem to lead to solipsism and self-focused orientation—academic-speak for a self-directed and even narcissistic outlook. When non-rich people are trying to determine what's right or wrong or how to behave, they tend to turn outward, basing their behavior on what others around them are doing and on social norms and rules. They often place value on acting in a moral way, doing the right thing and being a good person. When the wealthy face decisions about how to act, on the other hand, they tend to prioritize their own internal desires and goals. This can mean prioritizing self-interest over the needs of others, and breaking rules that stand in the way of those interests. They are more likely to break the law when driving, or, in lab studies, to lie in negotiations or cheat to get ahead. They also tend to be less empathetic to others and less able to judge emotions in both photographs and in-person interactions. Finally—and counterintuiviely—in laboratory experiments they are less generous with money than poorer people.
This longitudinal cohort study investigates whether atypical structural development in areas of the brain tied to school readiness skills mediates the relationship between childhood poverty and impaired academic performance.
There's a saying among futurists that a human-equivalent artificial intelligence will be our last invention. After that, AIs will be capable of designing virtually anything on their own — including themselves. Here's how a recursively self-improving AI could transform itself into a superintelligent machine.
Can machines be creative? Recent successes in AI have shown that machines can now perform at human levels in many tasks that, just a few years ago, were considered to be decades away, like driving cars, understanding spoken language, and recognizing objects. But these are all tasks where we know what needs to be done, and the machine is just imitating us. What about tasks where the right answers are not known? Can machines be programmed to find solutions on their own, and perhaps even come up with creative solutions that humans would find difficult?
he authors examine how susceptible jobs are to computerisation, by implementing a novel methodology to estimate the probability of computerisation for 702 detailed occupations, using a Gaussian process classifier. Based on these estimates, they examine expected impacts of future computerisation on US labour market outcomes, with the primary objective of analysing the number of jobs at risk and the relationship between an occupation’s probability of computerisation, wages and educational attainment. According to their estimates, about 47 per cent of total US employment is at risk. They further provide evidence that wages and educational attainment exhibit a strong negative relationship with an occupation’s probability of computerisation.
The biological world is computational at its core, argues computer scientist Leslie Valiant. His “ecorithm” approach uses computational concepts to explore fundamental mysteries of evolution and the mind.
Complex systems may have billion components making consensus formation slow and difficult. Recently several overlapping stories emerged from various disciplines, including protein structures, neuroscience and social networks, showing that fast responses to known stimuli involve a network core of few, strongly connected nodes. In unexpected situations the core may fail to provide a coherent response, thus the stimulus propagates to the periphery of the network. Here the final response is determined by a large number of weakly connected nodes mobilizing the collective memory and opinion, i.e. the slow democracy exercising the 'wisdom of crowds'. This mechanism resembles to Kahneman's "Thinking, Fast and Slow" discriminating fast, pattern-based and slow, contemplative decision making. The generality of the response also shows that democracy is neither only a moral stance nor only a decision making technique, but a very efficient general learning strategy developed by complex systems during evolution. The duality of fast core and slow majority may increase our understanding of metabolic, signaling, ecosystem, swarming or market processes, as well as may help to construct novel methods to explore unusual network responses, deep-learning neural network structures and core-periphery targeting drug design strategies.
(Illustrative videos can be downloaded from here:this http URL)
Fast and slow thinking -- of networks: The complementary 'elite' and 'wisdom of crowds' of amino acid, neuronal and social networks Peter Csermely
In 1990, researchers Patrick Kyllonen and Raymond Christal found a striking correlation. They gave large groups of American Air Force recruits various tests of working memory, in which participants performed simple operations on a single letter.
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