Aesthetic preference for art emerges from a weighted integration over hierarchically structured visual features in the brain

It is an open question whether preferences for visual art can be lawfully predicted from the basic constituent elements of a visual image. Moreover, little is known about how such preferences are actually constructed in the brain. Here we developed and tested a computational framework to gain an understanding of how the human brain constructs aesthetic value. We show that it is possible to explain human preferences for a piece of art based on an analysis of features present in the image. This was achieved by analyzing the visual properties of drawings and photographs by multiple means, ranging from image statistics extracted by computer vision tools, subjective human ratings about attributes, to a deep convolutional neural network. Crucially, it is possible to predict subjective value ratings not only within but also across individuals, speaking to the possibility that much of the variance in human visual preference is shared across individuals. Neuroimaging data revealed that preference computations occur in the brain by means of a graded hierarchical representation of lower and higher level features in the visual system. These features are in turn integrated to compute an overall subjective preference in the parietal and prefrontal cortex. Our findings suggest that rather than being idiosyncratic, human preferences for art can be explained at least in part as a product of a systematic neural integration over underlying visual features of an image. This work not only advances our understanding of the brain-wide computations underlying value construction but also brings new mechanistic insights to the study of visual aesthetics and art appreciation

Image analysis and statistical modelling for measurement and quality assessment of ornamental horticulture crops in glasshouses

Image analysis for ornamental crops is discussed with examples from the bedding plant industry. Feed-forward artificial neural networks are used to segment top and side view images of three contrasting species of bedding plants. The segmented images provide objective measurements of leaf and flower cover, colour, uniformity and leaf canopy height. On each imaging occasion, each pack was scored for quality by an assessor panel and it is shown that image analysis can explain 88.5%, 81.7% and 70.4% of the panel quality scores for the three species, respectively. Stereoscopy for crop height and uniformity is outlined briefly. The methods discussed here could be used for crop grading at marketing or for monitoring and assessment of growing crops within a glasshouse during all stages of production

Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions.

Fall prediction is a multifaceted problem that involves complex interactions between physiological, behavioral, and environmental factors. Existing fall detection and prediction systems mainly focus on physiological factors such as gait, vision, and cognition, and do not address the multifactorial nature of falls. In addition, these systems lack efficient user interfaces and feedback for preventing future falls. Recent advances in internet of things (IoT) and mobile technologies offer ample opportunities for integrating contextual information about patient behavior and environment along with physiological health data for predicting falls. This article reviews the state-of-the-art in fall detection and prediction systems. It also describes the challenges, limitations, and future directions in the design and implementation of effective fall prediction and prevention systems

The mind-body problem; three equations and one solution represented by immaterial-material data

Human life occurs within a complex bio-psycho-social milieu, a heterogeneous system that is integrated by multiple bidirectional interrelations existing between the abstract-intangible ideas and physical-chemical support of environment. The mind is thus placed between the abstract ideas/ concepts and neurobiological brain that is further connected to environment. In other words, the mind acts as an interface between the immaterial (abstract/ intangible) data and material (biological) support. The science is unable to conceives and explains an interaction between the immaterial and material domains (to understand nature of the mind), this question generating in literature the mind-body problem. We have published in the past a succession of articles related to the mind-body problem, in order to demonstrate the fact that this question is actually a false issue. The phenomenon of immaterial-material interaction is impossible to be explained because it never occurs, which means that there is no need to explain the immaterial-material interaction. Our mind implies only a temporal association between the immaterial data and material support, this dynamic interrelation being presented and argued here as a solution to the mind-body problem. The limited psycho-biologic approach of the mind-body problem is expanded here to a more comprehensive and feasible bio-psycho-social perspective, generating thus three distinct (bio- psychological, bio-social, and psycho-social) equations. These three equations can be solved through a solution represented by a dynamic cerebral system (two distinct and interconnected subunits of the brain) which presumably could have the capability of receiving and processing abstract data through association (with no interaction) between immaterial and material data

Die Rolle der Zielnähe und der investierten Anstrengung für den erwarteten Wert einer Handlung

In human neuroscientific research, there has been an increasing interest in how the brain computes the value of an anticipated outcome. However, evidence is still missing about which valuation related brain regions are modulated by the proximity to an expected goal and the previously invested effort to reach a goal. The aim of this dissertation is to investigate the effects of goal proximity and invested effort on valuation related regions in the human brain. We addressed this question in two fMRI studies by integrating a commonly used reward anticipation task in differential versions of a Multitrial Reward Schedule Paradigm. In both experiments, subjects had to perform consecutive reward anticipation tasks under two different reward contingencies: in the delayed condition, participants received a monetary reward only after successful completion of multiple consecutive trials. In the immediate condition, money was earned after every successful trial. In the first study, we could demonstrate that the rostral cingulate zone of the posterior medial frontal cortex signals action value contingent to goal proximity, thereby replicating neurophysiological findings about goal proximity signals in a homologous region in non-human primates. The findings of the second study imply that brain regions associated with general cognitive control processes are modulated by previous effort investment. Furthermore, we found the posterior lateral prefrontal cortex and the orbitofrontal cortex to be involved in coding for the effort-based context of a situation. In sum, these results extend the role of the human rostral cingulate zone in outcome evaluation to the continuous updating of action values over a course of action steps based on the proximity to the expected reward. Furthermore, we tentatively suggest that previous effort investment invokes processes under the control of the executive system, and that posterior lateral prefrontal cortex and the orbitofrontal cortex are involved in an effort-based context representation that can be used for outcome evaluation that is dependent on the characteristics of the current situation.Derzeit besteht im Bereich der Neurowissenschaften ein großes Interesse daran aufzuklären, auf welche Weise verschiedene Variablen die Wertigkeit eines erwarteten Handlungsziels beeinflussen bzw. welche Hirnregionen an der Repräsentation der Wertigkeit eines Handlungsziels beteiligt sind. Die meisten Untersuchungen beziehen sich dabei auf Einflussgrößen wie die erwartete Belohnungshöhe, die Wahrscheinlichkeit, mit der ein bestimmtes Ereignis eintritt, oder die Dauer bis zum Erhalt einer Belohnung. Bisher liegen jedoch kaum Untersuchungen vor bezüglich zweier anderer Variablen, die ebenfalls den erwarteten Wert eines Handlungsergebnisses beeinflussen. Das sind (a) die Nähe zu dem erwarteten Ziel und (b) die bisher investierte Anstrengung, um ein Ziel zu erreichen. Das Ziel der vorliegenden Dissertation ist zu untersuchen, wie die Nähe zum Ziel und die bisher investierte Anstrengung Gehirnregionen beeinflussen, die mit der Repräsentation von Wertigkeit im Zusammenhang stehen. Dazu führten wir zwei fMRT-Studien durch, in denen wir eine klassische Belohnungs-Antizipationsaufgabe in unterschiedliche Versionen eines „Multitrial Reward Schedule“ Paradigmas integriert haben. Das bedeutet, dass die Probanden Belohnungs-Antizipationsaufgaben unter zwei unterschiedlichen Belohnungskontingenzen bearbeiteten: In der verzögerten Bedingung erhielten die Probanden einen Geldbetrag nach der erfolgreichen Bearbeitung von mehreren aufeinanderfolgenden Aufgaben, in der direkten Bedingung dagegen nach jeder korrekt ausgeführten Aufgabe. In der ersten Studie konnte eine sukzessiv ansteigende Aktivität in Abhängigkeit zur Zielnähe in der rostralen cingulären Zone identifiziert werden. Das deutet darauf hin, dass dieses Areal den Wert einer Handlung in Abhängigkeit zur Nähe zum Ziel kodiert. Die Ergebnisse der zweiten Studie zeigten, dass die bisher investierte Anstrengung kortikale Regionen moduliert, die klassischerweise mit kognitiven Kontrollfunktionen in Zusammenhang gebracht werden. Außerdem repräsentierten der posteriore laterale präfrontale Cortex und der orbitofrontale Cortex den motivationalen Kontext eines Trials anhand des Risikos des Verlustes von bisher investierter Anstrengung. Insgesamt weisen diese Befunde darauf hin, dass die rostrale cinguläre Zone eine entscheidende Rolle spielt für die Kontrolle sequenzieller Handlungsstufen, die auf eine verzögerte Belohnung ausgerichtet sind. Diese Kontrollfunktion scheint auf der kontinuierlichen Aktualisierung des Wertes einer Handlungsstufe zu basieren, der von der aktuellen Zielnähe bestimmt wird. Die Befunde der zweiten Studie lassen darauf schließen, dass sich die bisher investierte Anstrengung zur Erreichung eines Handlungsziels auf die Bereitstellung von allgemeinen kognitiven Ressourcen auswirkt. Das Risiko des Verlustes von bisher investierter Anstrengung kann außerdem ein kontextuelles Merkmal der Situation darstellen, das als Bezugsrahmen für die Evaluation des erwarteten Wertes dienen kann

Plug-in to fear: game biosensors and negative physiological responses to music

The games industry is beginning to embark on an ambitious journey into the world of biometric gaming in search of more exciting and immersive gaming experiences. Whether or not biometric game technologies hold the key to unlock the “ultimate gaming experience” hinges not only on technological advancements alone but also on the game industry’s understanding of physiological responses to stimuli of different kinds, and its ability to interpret physiological data in terms of indicative meaning. With reference to horror genre games and music in particular, this article reviews some of the scientific literature relating to specific physiological responses induced by “fearful” or “unpleasant” musical stimuli, and considers some of the challenges facing the games industry in its quest for the ultimate “plugged-in” experience

Artificial neural network (ANN) enabled internet of things (IoT) architecture for music therapy

Alternative medicine techniques such as music therapy have been a recent interest of medical practitioners and researchers. Significant clinical evidence suggests that music has a positive influence over pain, stress and anxiety for the patients of cancer, pre and post surgery, insomnia, child birth, end of life care, etc. Similarly, the technologies of Internet of Things (IoT), Body Area Networks (BAN) and Artificial Neural Networks (ANN) have been playing a vital role to improve the health and safety of the population through offering continuous remote monitoring facilities and immediate medical response. In this article, we propose a novel ANN enabled IoT architecture to integrate music therapy with BAN and ANN for providing immediate assistance to patients by automating the process of music therapy. The proposed architecture comprises of monitoring the body parameters of patients using BAN, categorizing the disease using ANN and playing music of the most appropriate type over the patient’s handheld device, when required. In addition, the ANN will also exploit Music Analytics such as the type and duration of music played and its impact over patient’s body parameters to iteratively improve the process of automated music therapy. We detail development of a prototype Android app which builds a playlist and plays music according to the emotional state of the user, in real time. Data for pulse rate, blood pressure and breath rate has been generated using Node-Red, and ANN has been created using Google Colaboratory (Colab). MQTT broker has been used to send generated data to Android device. The ANN uses binary and categorical cross-entropy loss functions, Adam optimiser and ReLU activation function to predict the mood of patient and suggest the most appropriate type of music

Self-organized learning in multi-layer networks

We present a framework for the self-organized formation of high level learning by a statistical preprocessing of features. The paper focuses first on the formation of the features in the context of layers of feature processing units as a kind of resource-restricted associative multiresolution learning We clame that such an architecture must reach maturity by basic statistical proportions, optimizing the information processing capabilities of each layer. The final symbolic output is learned by pure association of features of different levels and kind of sensorial input. Finally, we also show that common error-correction learning for motor skills can be accomplished also by non-specific associative learning. Keywords: feedforward network layers, maximal information gain, restricted Hebbian learning, cellular neural nets, evolutionary associative learnin