Visual illusions: An interesting tool to investigate developmental dyslexia and autism spectrum disorder

A visual illusion refers to a percept that is different in some aspect from the physical stimulus. Illusions are a powerful non-invasive tool for understanding the neurobiology of vision, telling us, indirectly, how the brain processes visual stimuli. There are some neurodevelopmental disorders characterized by visual deficits. Surprisingly, just a few studies investigated illusory perception in clinical populations. Our aim is to review the literature supporting a possible role for visual illusions in helping us understand the visual deficits in developmental dyslexia and autism spectrum disorder. Future studies could develop new tools – based on visual illusions – to identify an early risk for neurodevelopmental disorders

Aerospace medicine and biology: A continuing bibliography with indexes, supplement 128, May 1974

This special bibliography lists 282 reports, articles, and other documents introduced into the NASA scientific and technical information system in April 1974

Constructivist and Ecological Rationality in Economics

When we leave our closet, and engage in the common affairs of life, (reason's) conclusions seem to vanish, like the phantoms of the night on the appearance of the morning; and 'tis difficult for us to retain even that conviction, which we had attained with difficulty (Hume, 1739/, p 507). we must constantly adjust our lives, our thoughts and our emotions, in order to live simultaneously within different kinds of orders according to different rules. If we were to apply the unmodified, uncurbed rules (of caring intervention to do visible 'good') of the small band or troop, or our families to the (extended order of cooperation through markets), as our instincts and sentimental yearnings often make us wish to do, we would destroy it. Yet if we were to always apply the (noncooperative) rules of the extended order to our more intimate groupings, we would crush them. (Hayek, 1988, p 18). (Italics are his, parenthetical reductions are mine).behavioral economics; experimental economics

Specializations of birds that attend army ant raids: an ecological approach to cognitive and behavioral studies.

Tropical birds forage at army ant raids on several continents. Obligate foraging at army ant raids evolved several times in the Neotropical true antbird family (Thamnophilidae), and recent evidence suggests a diversity of bird species from other families specialize to varying degrees on army ant exploitation. Army ant raids offer access to high prey densities, but the ant colonies are mobile and widely spaced. Successful army ant exploitation requires solving a complex foraging problem because army ant raids are unpredictable in space and time. Birds can counteract the challenges posed by the ants by using strategies that raise their chances of detecting army ant raids, and birds can use additional strategies to track army ant colonies they have located. Some features of army ant biology, such as their conspicuous swarms and columns, above-ground activity, and regular cycles of behavior, provide opportunities for birds to increase their effectiveness at exploiting raids. Changes in sensory, cognitive and behavioral systems may all contribute to specialized army ant exploitation in a bird population. The combination of specializations that are employed may vary independently among bird species and populations. The degree of army ant exploitation by birds varies geographically with latitude and elevation, and with historical patterns such as centers of distribution of obligate thamnophilid antbirds. We predict the set of specializations a given bird population exhibits will depend on local ecology, as well as phylogenetic history. Comparative approaches that focus on these patterns may indicate ecological and evolutionary factors that have shaped the costs and benefits of this foraging strategy. The development of army ant exploitation in individual birds is poorly understood, and individual expression of these specializations may depend on a combination of genetic adaptation with cognitive plasticity, possibly including social and experiential learning. Future studies that measure developmental changes and quantify individual differences in army ant exploitation are needed to establish the mechanisms underlying this behavior.This work was supported by National Science Foundation grant IOS-1209072 and Drexel University funds (to S.O’D.); the Gates Cambridge Scholarship and Murray Edwards College Overseas Bursary (to C.L.); and the University of Cambridge, Clare College and BBSRC (to N.S.C.).This is the author accepted manuscript. The final version is available from Elsevier via http://dx.doi.org/10.1016/j.beproc.2012.09.00

Predictive Analysis of Vector-Borne Diseases through Tabular Classification of Epidemiological Data

Vector-borne diseases (VBDs) are major threats to human health. They are estimated to cause more than 700,000 deaths each year. This presents serious health problems for CBD. In recent years, the incidence of VBDs has increased globally, affecting one billion people approximately and accounting for 17% of all infectious diseases. Globally, disease rates have risen at an alarming rate, with more than 3.9 billion people at risk of infection. Therefore, it is essential to find approaches to detect these diseases; this is where machine learning (ML) models come into play. The purpose of this study was to predict VBDs using tabular epidemiological data. For this purpose, a set of ML models was used, such as support vector classifier (SVC), extreme gradient boosting (XGBoost), LightGBM, CatBoost, random forest (RF), and balanced random forest (BRF). A dataset consisting of 65 features and 1262 records was used during the training stage. The results highlighted the successful integration of the different models, such as SVC, XGBoost, LightGBM, CatBoost, BRF, and RF, with weights of 0.49959 ± 0.27112, 0.58496 ± 0.22619, 0.48482 ± 0.29971, 0.54992 ± 0.27982, 0.24924 ± 0.22654, and 0.45592 ± 0.25849. In addition, the BRF model stood out for having the lowest log loss, evaluated through the ensemble log-loss metric, with an average of 0.24924 and a standard deviation of 0.22654

Detection of tongue protrusion gestures from videos

We propose a system that, using video information, segments the mouth region from a face image and then detects the protrusion of the tongue from inside the oral cavity. Initially, under the assumption that the mouth is closed, we detect both mouth corners. We use a set of specifically oriented Gabor filters for enhancing horizontal features corresponding to the shadow existing between the upper and lower lips. After applying the Hough line detector, the extremes of the line that was found are regarded as the mouth corners. Detection rate for mouth corner localization is 85.33%. These points are then input to a mouth appearance model which fits a mouth contour to the image. By segmenting its bounding box we obtain a mouth template. Next, considering the symmetric nature of the mouth, we divide the template into right and left halves. Thus, our system makes use of three templates. We track the mouth in the following frames using normalized correlation for mouth template matching. Changes happening in the mouth region are directly described by the correlation value, i.e., the appearance of the tongue in the surface of the mouth will cause a decrease in the correlation coefficient through time. These coefficients are used for detecting the tongue protrusion. The right and left tongue protrusion positions will be detected by analyzing similarity changes between the right and left half-mouth templates and the currently tracked ones. Detection rates under the default parameters of our system are 90.20% for the tongue protrusion regardless of the position, and 84.78% for the right and left tongue protrusion positions. Our results demonstrate the feasibility of real-time tongue protrusion detection in vision-based systems and motivates further investigating the usage of this new modality in human-computer communication